Tarefa paper_repro.mmv_2011.tab20_1_c1.design

Veredito

Pergunta feita ao analista

Do government cash transfers buy political goodwill? In Uruguay's PANES programme, households qualified for monthly transfers when their income measure fell below an eligibility cutoff. Using the households near that cutoff, estimate the effect of receiving the transfers on the household's backing for the government.

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   Do government cash transfers buy political goodwill? In Uruguay's PANES programme, households qualified for monthly transfers when their income measure fell below an eligibility cutoff. Using the households near that cutoff, estimate the effect of receiving the transfers on the household's backing for the government.

   gov_transfers (agent)

   Households near the income cutoff of Uruguay's PANES cash-transfer programme, the extract of the survey of Manacorda, Miguel and Vigorito (2011), 'Government Transfers and Political Support', distributed with the causaldata R package and used in chapter 20 of Huntington-Klein's 'The Effect': one row per household (1,948 rows), only households whose income measure is within 0.02 of the cutoff. Households below the cutoff were eligible for the transfers. Columns: rownames, the row number; Income_Centered, the household's income measure minus the eligibility cutoff (negative values are below the cutoff, hence eligible); Income_Below, minus Income_Centered, added by the task author: how far below the cutoff the household's income measure is (positive values are eligible); Education, average years of education of household members aged 16 and older (missing for some households); Age, average age of household members; Participation, 1 if the household participated in the transfer programme; Support, the household's support for the government (0, 0.5 or 1; higher means more support).

   file 51312ecd5b3cb22b5dc027966a81e0a8579f24a708ce4b476674fc527652ebd0 · frame —

   1. derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}
2. 1Transformação
   gov_transfers · derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}

   1948 → 1948 linhas

   Frame antes → depois: 2600b9da8866 → ee25bc867239
3. 2Turno do modelo
   call_7e63ec01gemini-3.8-flashFim: STOP6.859 ms14354 de entrada · 45 de saída · 264 de raciocínio · 0 em cache

   Texto visível

   I will load the regression-discontinuity skill to follow the standard procedure for evaluating cutoff-based eligibility rules and checking for manipulation.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing Government Cash Transfers and Political Goodwill** My objective here is to determine whether government cash transfer programs effectively cultivate political goodwill. The specific context I'm examining is Uruguay's PANES program, which provides monthly transfers to households whose incomes fall below a defined eligibility threshold. My core task is to estimate the impact of receiving these transfers on a household's support for the government. To do this, I'll be leveraging the `gov_transfers` dataset. I've reviewed its structure and identified the key variables: * `rownames`: A unique identifier for each household. * `Income_Centered`: This is crucial. It represents household income relative to the eligibility cutoff. Negative values mean eligibility, and positive values mean ineligibility. This variable will serve as my "running variable" in an RDD analysis. * `Income_Below`: This seems to be a binary indicator derived from `Income_Centered`, where positive values confirm eligibility. I'll need to clarify its exact relationship with `Income_Centered` and whether it's redundant or provides an alternative way to define the eligibility rule. * `Education`, `Age`: These are demographic control variables that might be important for understanding household characteristics. * `Participation`: This indicates whether a household actually participated in the transfer program. This is a critical outcome variable for assessing the program's reach. * `Support`: This is my primary dependent variable, measuring household support for the government on a scale of 0, 0.5, or 1. Immediately, the design of the PANES program – with a clear cutoff for eligibility – screams **Regression Discontinuity Design (RDD)**. Households very close to the cutoff, on either side, are likely very similar in unobserved characteristics, making the difference in treatment status (receiving transfers) as good as random assignment for these individuals. This is exactly the setting RDD is designed for. I need to be precise about whether this is a **sharp RDD** (where eligibility perfectly predicts treatment – everyone below the cutoff *gets* the transfer) or a **fuzzy RDD** (where eligibility only *increases the probability* of receiving the transfer, perhaps due to take-up issues or administrative hurdles). The presence of `Participation` as a separate variable suggests it might be a fuzzy RDD, as not all eligible households might participate. If it is fuzzy, I might need to consider **instrumental variables** techniques, where eligibility (being below the cutoff) serves as an instrument for actual participation. Therefore, my immediate next step is to load the `regression-discontinuity` skill. Depending on the nature of the RDD (sharp vs. fuzzy), I might then explore the `instrumental-variables` skill or the `choose-identification-strategy` skill to confirm the most appropriate analytical approach. My focus will be on identifying the causal effect of *receiving* the transfer (or being eligible for it, depending on the RDD type) on `Support`.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "regression-discontinuity"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
4. 3Habilidade
   regression-discontinuityTexto da habilidade
5. 4Turno do modelo
   call_7505eb9agemini-3.8-flashFim: STOP5.239 ms15156 de entrada · 52 de saída · 130 de raciocínio · 0 em cache

   Texto visível

   I will inspect the variables in the `gov_transfers` dataset to check sample sizes, the distribution of the running variable, participation rates, and missingness.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding the Transfer Impact Study** Right, before I dive into modeling, the absolute first thing I need to nail down is the *identification strategy*. The core question here is estimating the causal effect of receiving these transfers on the household's backing for the government. This isn't just about correlation; I need to isolate the *effect* of the transfers themselves. And that brings me to the crucial distinction: is this a sharp Regression Discontinuity Design (RDD) or a fuzzy RDD? Or, perhaps, are instrumental variables a more appropriate approach given the data? I need to figure out which of these causal inference frameworks best fits the way these transfers are allocated. Now, let's get specific about the "receiving the transfers" part. The prompt clearly states: "`Participation` is actual receipt (1 if participated)." This is the key outcome variable I'm interested in *as the treatment*. The critical question then becomes: how does this `Participation` variable behave around the eligibility cutoff? Does participation jump cleanly from 0 to 1 precisely at the cutoff point? If so, that would strongly suggest a sharp RDD, making the identification relatively straightforward. However, it's far more likely, especially in real-world policy settings, that participation isn't deterministic. I need to investigate if participation is fuzzy. Does everyone who is eligible actually receive the transfer? Or is there a probability of receiving it that's higher for those just above the cutoff but not necessarily 100%? To answer this, I need to scrutinize the data. My immediate next step is to run `data.describe`. This will give me a crucial overview of the distributions of my variables, including the means, minimums, maximums, and standard deviations. This initial exploration is paramount. Specifically, I'll be looking at the `Participation` variable in relation to the eligibility cutoff. If `data.describe` shows that `Participation` is not strictly 1 for everyone above the cutoff, or not strictly 0 for everyone below, then I'm definitely dealing with a fuzzy RDD scenario. It's also a good way to spot any immediate data anomalies or distributions that might complicate analysis. I need to understand the empirical reality of how the policy is implemented before I can confidently choose and implement my estimation strategy.

   Chamadas pedidas

   • data.describe

     {
       "dataset": "gov_transfers"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
6. 5Ferramenta
   data.describev0.1.0call_4e3a04b7decidido pelo modeloreexecutável

   Motivo: I will inspect the variables in the `gov_transfers` dataset to check sample sizes, the distribution of the running variable, participation rates, and missingness.

   Argumentos

   {
     "columns": null,
     "dataset": "gov_transfers"
   }

   Figuras call_4e3a04b7

   Chave	Valor	Valor bruto	Erro-padrão	IC
   n_rows Rows in the dataset	1.948 rows	1948	—	—

   Tabelas (1)

   {
     "columns": [
       {
         "column": "rownames",
         "dtype": "int64",
         "max": 1948,
         "mean": 974.5,
         "min": 1,
         "n_missing": 0,
         "n_unique": 1948,
         "sd": 562.4834812389308
       },
       {
         "column": "Income_Centered",
         "dtype": "float64",
         "max": 0.019892002,
         "mean": -0.0015801258619681732,
         "min": -0.019990994,
         "n_missing": 0,
         "n_unique": 1480,
         "sd": 0.011630973423832775
       },
       {
         "column": "Education",
         "dtype": "float64",
         "max": 6.5,
         "mean": 4.070623415324197,
         "min": 0,
         "n_missing": 51,
         "n_unique": 38,
         "sd": 1.53078338867832
       },
       {
         "column": "Age",
         "dtype": "float64",
         "max": 73,
         "mean": 29.124286594045177,
         "min": 6.5,
         "n_missing": 0,
         "n_unique": 439,
         "sd": 14.42839100741896
       },
       {
         "column": "Participation",
         "dtype": "int64",
         "max": 1,
         "mean": 0.5785420944558521,
         "min": 0,
         "n_missing": 0,
         "n_unique": 2,
         "sd": 0.49391939997995227
       },
       {
         "column": "Support",
         "dtype": "float64",
         "max": 1,
         "mean": 0.7962012320328542,
         "min": 0,
         "n_missing": 0,
         "n_unique": 3,
         "sd": 0.31816426544052556
       },
       {
         "column": "Income_Below",
         "dtype": "float64",
         "max": 0.019990994,
         "mean": 0.0015801258619681732,
         "min": -0.019892002,
         "n_missing": 0,
         "n_unique": 1480,
         "sd": 0.011630973423832775
       }
     ]
   }

   Digests dos dados

   gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

   sha256 do resultado

   ae331c4936c92ed94455f18e236dd61579ee3e81d899da8a83cf5da5e09b5a85

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_5c7bc6d8gemini-3.8-flashFim: STOP11.440 ms15678 de entrada · 70 de saída · 1072 de raciocínio · 12608 em cache

   Texto visível

   I will run the McCrary density test on `Income_Below` at the cutoff 0 to check whether there is any sorting or manipulation of the running variable around the eligibility threshold.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Applying Regression Discontinuity Design** Okay, so I'm trying to figure out the best way to apply regression discontinuity (RD) here, and I'm looking at `infer.rdd` and its documentation. It tells me `infer.rdd` estimates a sharp RD effect using local linear (or quadratic) fits on either side of a cutoff, with a default bandwidth. It's suitable when eligibility or treatment switches at a known threshold of a running variable – like income or test scores – and the question is about the effect for units *near* that threshold. I need to be careful though. The documentation explicitly warns *against* using it if units can manipulate the running variable around the cutoff. In that case, I should run `infer.density_test` first. It also notes that if the treatment probability jumps but not from 0 to 1 (a fuzzy design), this version of `infer.rdd` *only* estimates the jump in the outcome, and I should report it as an "intention to treat" (ITT) effect. This immediately makes me wonder: is my situation sharp or fuzzy? I'm thinking about running `infer.rdd` with different outcomes, like `'Support'` and `'Participation'`, to see what emerges. I also recall referencing Huntington-Klein's "The Effect," specifically Chapter 20 on Regression Discontinuity. There's an example there using data from Manacorda, Miguel, and Vigorito (2011) concerning Uruguay's PANES cash-transfer program. This dataset, available in the `causaldata` R package, focuses on households near an income cutoff, where being *below* the cutoff made them eligible for transfers. This brings me back to the core question: does `infer.rdd` (or Huntington-Klein's approach in that chapter) handle sharp or fuzzy RD, and how does it define the "treated" group? The `infer.rdd` documentation explicitly states for fuzzy designs: "the treatment probability jumps but not from 0 to 1 (fuzzy design): this version estimates the jump in the outcome only; report it as intention to treat." I'm also considering if `infer.iv_2sls` could be used for fuzzy RD, but I'm not immediately clear on what the instruments and controls would be in this context, and how that relates to Chapter 20. So, my immediate plan is to first run `infer.rdd` to see the raw outcome effect, and simultaneously run `infer.density_test` to check for manipulation of the running variable. Now, which running variable should I use? The dataset I'm looking at, `gov_transfers`, has `Income_Centered`. This variable represents household income *minus* the eligibility cutoff, so negative values are below the cutoff (eligible) and positive values are above (ineligible). But here's a critical thought: `infer.rdd` likely assumes a specific convention for the running variable, possibly that values *above* the cutoff represent treatment. Let's check the `infer.rdd` documentation again. It states `cutoff: Threshold of the running variable at which treatment switches (default 0)` and describes "Local linear (or quadratic) fits on each side within a bandwidth." The question is: does "each side" imply left is control/right is treated, or vice-versa? This leads me to examine `Income_Below`. This variable was *added* by the task author. Its description is: "`Income_Below`, minus `Income_Centered`, added by the task author: how far below the cutoff the household's income measure is (positive values are eligible)." Ah, this is key! The task author explicitly created `Income_Below` so that *positive values indicate eligibility (treatment)*. This is likely because standard RD implementations (and perhaps the underlying assumptions of `infer.rdd`) often treat the region *above* the cutoff as the treated group, or at least need a running variable where the sign of the difference from the cutoff directly maps to treatment status in a predictable way. By using `Income_Below`, with a cutoff of 0, positive values are clearly on the "treated" side according to this newly defined variable. Therefore, my next step is to run `infer.rdd` using `Income_Below` as the running variable, with `cutoff=0`, and also run `infer.density_test` with the same parameters. This should align with how `infer.rdd` likely expects the data to be structured for a straightforward sharp RD analysis, while the density test will help confirm the absence of manipulation.

   Chamadas pedidas

   • infer.density_test

     {
       "cutoff": 0,
       "dataset": "gov_transfers",
       "running": "Income_Below"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
8. 7Ferramenta
   infer.density_testv0.1.0call_ba2c1148decidido pelo modeloreexecutável

   Motivo: I will run the McCrary density test on `Income_Below` at the cutoff 0 to check whether there is any sorting or manipulation of the running variable around the eligibility threshold.

   Argumentos

   {
     "bandwidth": null,
     "bin_size": null,
     "cutoff": 0,
     "dataset": "gov_transfers",
     "running": "Income_Below"
   }

   Figuras call_ba2c1148

   Chave	Valor	Valor bruto	Erro-padrão	IC
   theta Log difference of the density of Income_Below just above and just below the cutoff	0,277737 log points	0.27773678426803583	0,1594550.15945502634327913	—
   se Standard error of theta	0,159455 log points	0.15945502634327913	—	—
   z z statistic of theta	1,74179	1.741787578838164	—	—
   p_value p-value of no density jump at the cutoff	0,0815456 probability	0.08154562097449766	—	—

   Diagnósticos

   • method = "mccrary"
   • manipulation = false
   • bin_size = 0,000527050.0005270497149457954
   • bandwidth = 0,009468250.009468247293525169
   • n_obs = 1.9481948
   • f_left = 17,988717.988736280041078
   • f_right = 23,747623.74762639144579
   • n_distinct = 1.4801480

   Digests dos dados

   gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

   sha256 do resultado

   da40fbd1c32c13380e1d36115755f86c93676f2d7dfea61276bee5087d169550

   Resultado mostrado ao modelo
9. 8Turno do modelo
   call_ca75d02bgemini-3.8-flashFim: STOP4.646 ms17110 de entrada · 76 de saída · 93 de raciocínio · 12757 em cache

   Texto visível

   I will run a sharp regression discontinuity design on `Support` using `Income_Below` as the running variable with cutoff 0 to estimate the jump in political support at the eligibility threshold.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding the Density Test and Inferential Analysis** Alright, so I've just completed the density test, and the key takeaway here is the p-value: 0.0815456. From my perspective, this value is greater than our standard significance threshold of 0.05. This immediately tells me that, at the 5% confidence level, I cannot reject the null hypothesis. In practical terms, this means there's no statistically significant jump in density that would suggest manipulation. So, for now, I'm concluding that manipulation is false based on this test. Now, my attention shifts to the `infer.rdd` analysis. I want to explore the relationship between my independent variable and both 'Participation' and 'Support' as dependent variables. My initial plan was to run `infer.rdd` with `y='Participation'` and then follow up with `y='Support'`. However, a more structured approach occurs to me. Before diving into both dependent variables simultaneously, I think it's more prudent to isolate and first examine the effect on 'Support' when considering the `Income_Below` segment. This will allow me to build a more focused understanding of that specific relationship before broadening the scope. Therefore, my immediate next step is to execute `infer.rdd` with `y='Support'` and specifically focus on the `Income_Below` subset. This sequential approach feels more robust and will likely yield cleaner insights.

   Chamadas pedidas

   • infer.rdd

     {
       "cutoff": 0,
       "dataset": "gov_transfers",
       "running": "Income_Below",
       "y": "Support"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
10. 9Ferramenta
    infer.rddv0.1.0call_2a1d3b0adecidido pelo modeloreexecutável

    Motivo: I will run a sharp regression discontinuity design on `Support` using `Income_Below` as the running variable with cutoff 0 to estimate the jump in political support at the eligibility threshold.

    Argumentos

    {
      "bandwidth": null,
      "cluster": null,
      "cutoff": 0,
      "dataset": "gov_transfers",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "Income_Below",
      "subset": null,
      "y": "Support"
    }

    Figuras call_2a1d3b0a

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Jump in Support at the cutoff of Income_Below	0,0166086	0.01660864823337719	0,04859680.04859678434028411	-0,0786393 – 0,111857

    Diagnósticos

    • bandwidth = 0,008458210.008458207971633918
    • bandwidth_method = "IK"
    • n_left = 341341
    • n_right = 473473
    • kernel = "triangular"
    • polynomial = 11
    • bandwidth_sensitive = false
    • n_obs = 1.9481948
    • se_type = "HC1"

    Tabelas (1)

    {
      "sensitivity": [
        {
          "bandwidth": 0.004229103985816959,
          "ci_high": 0.11843287120008139,
          "ci_low": -0.21293016732705689,
          "estimate": -0.047248648063487754,
          "multiplier": 0.5,
          "n_left": 149,
          "n_right": 232,
          "se": 0.08453294069199425
        },
        {
          "bandwidth": 0.008458207971633918,
          "ci_high": 0.11185659530479414,
          "ci_low": -0.07863929883803975,
          "estimate": 0.01660864823337719,
          "multiplier": 1,
          "n_left": 341,
          "n_right": 473,
          "se": 0.04859678434028411
        },
        {
          "bandwidth": 0.016916415943267836,
          "ci_high": 0.15387746597583843,
          "ci_low": 0.021027509410800338,
          "estimate": 0.08745248769331938,
          "multiplier": 2,
          "n_left": 675,
          "n_right": 954,
          "se": 0.03389091779566911
        }
      ]
    }

    Digests dos dados

    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

    sha256 do resultado

    d0bac3f010a0ded4617b3c784f30a83413c35ebe7ac4315d6b0dfc7368c2a2a6

    Resultado mostrado ao modelo
11. 10Turno do modelo
    call_3e16835fgemini-3.8-flashFim: STOP4.732 ms17704 de entrada · 66 de saída · 141 de raciocínio · 15926 em cache

    Texto visível

    I will estimate the jump in `Participation` at the cutoff of `Income_Below` to assess whether the design is sharp or fuzzy.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the Research Question on Government Cash Transfers and Political Goodwill** Alright, the core of this research question immediately grabs my attention. The prompt asks: "Do government cash transfers buy political goodwill? In Uruguay's PANES programme, households qualified for monthly transfers when their income measure fell below an eligibility cutoff. Using the households near that cutoff, estimate the effect of receiving the transfers on the household's backing for the government." The very first thing that jumps out at me is the explicit objective: "estimate the effect of receiving the transfers on the household's backing for the government!" This is the crux of the inquiry, the dependent variable we're aiming to quantify. But then, a critical methodological question arises in my mind: *how* is this effect being estimated? The description of the PANES program hinges on an "eligibility cutoff" based on income. This immediately brings to mind the concept of Regression Discontinuity Design (RDD). So, I ask myself: Is this a sharp RDD or a fuzzy RDD? In a sharp RDD, eligibility for the treatment (in this case, receiving the cash transfers) is a deterministic function of the running variable (income measure relative to the cutoff). If you're below the cutoff, you get it; if you're above, you don't. However, in a fuzzy RDD, the probability of receiving the treatment *jumps* at the cutoff but isn't necessarily 0 or 1. There might be some individuals just above the cutoff who still get the transfer for other reasons, or some below who, for various reasons, don't. Given this, my immediate next step is to look for evidence of this jump. I need to ascertain whether the receipt of transfers changes discontinuously at the eligibility threshold. The most direct way to explore this is by examining the `Participation` variable, which I presume represents the household's engagement with or receipt of the program's benefits. Therefore, my instinct is to run `infer.rdd` with `y='Participation'`. This command, I assume, will allow me to directly visualize and quantify the jump in the probability of participation at the cutoff point. This initial analysis is crucial for determining whether the RDD is sharp or fuzzy, which in turn dictates the appropriate econometric approach for estimating the causal effect on political goodwill.

    Chamadas pedidas

    • infer.rdd

      {
        "cutoff": 0,
        "dataset": "gov_transfers",
        "running": "Income_Below",
        "y": "Participation"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
12. 11Ferramenta
    infer.rddv0.1.0call_589bb090decidido pelo modeloreexecutávelnot_applicable

    Motivo: I will estimate the jump in `Participation` at the cutoff of `Income_Below` to assess whether the design is sharp or fuzzy.

    Argumentos

    {
      "bandwidth": null,
      "cluster": null,
      "cutoff": 0,
      "dataset": "gov_transfers",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "Income_Below",
      "subset": null,
      "y": "Participation"
    }

    too few observations near the cutoff to estimate the curvature for IK

    Digests dos dados

    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

    sha256 do resultado

    d81b6b13140e464ac5aaead6162309e164fbe4d7a17de9d9bdcc6f4f4e47d3c8

    Resultado mostrado ao modelo
13. 12Turno do modelo
    call_f3c67501gemini-3.8-flashFim: STOP4.124 ms17937 de entrada · 51 de saída · 116 de raciocínio · 15941 em cache

    Texto visível

    I will run an OLS regression to see the relationship between participation and the eligibility cutoff.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the IK Curvature Estimate** I've just encountered a puzzling output: "too few observations near the cutoff to estimate the curvature for IK." This immediately flags a concern in my mind, especially given the nature of the `IK` metric, which I understand requires a certain density of data around a crucial threshold. My initial hypothesis is that the issue stems from the `Participation` variable. Why? Because `Participation` is binary – it's either 0 or 1. This inherent discreteness, or potentially perfect compliance in certain scenarios, could be the very reason we lack the nuanced observations needed to accurately gauge curvature. To confirm this, I need to investigate whether `Participation` is behaving deterministically. This means I need to rigorously test its relationship with other relevant variables. A key step here is to examine the output of `infer.ols` applied to `Participation`. Specifically, I want to see how `Participation` behaves when regressed against `Income_Below`. If `infer.ols` with `y='Participation'` and `x=['Income_Below']` reveals a strong, perhaps almost perfect, linear relationship, or if a subset analysis like `subset='Income_Below > 0'` shows very little variation in `Participation` within the relevant income ranges, that would strongly support my suspicion. This kind of deterministic behavior would explain the lack of observations "near the cutoff" that `IK`'s curvature estimation so critically relies upon.

    Chamadas pedidas

    • infer.ols

      {
        "dataset": "gov_transfers",
        "x": [
          "Income_Below"
        ],
        "y": "Participation"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
14. 13Ferramenta
    infer.olsv0.1.0call_8545918fdecidido pelo modeloreexecutável

    Motivo: I will run an OLS regression to see the relationship between participation and the eligibility cutoff.

    Argumentos

    {
      "cluster": null,
      "controls": [],
      "dataset": "gov_transfers",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Income_Below"
      ],
      "y": "Participation"
    }

    Figuras call_8545918f

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Income_Below Coefficient on Income_Below in the regression of Participation	36,9973	36.99728644009521	0,3019810.30198102503461	36,405 – 37,5895

    Diagnósticos

    • n_obs = 1.9481948
    • k = 22
    • r2 = 0,7590310.7590313287691577
    • df_resid = 1.9461946
    • se_type = "HC1"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.5309909753106791,
          "ci_low": 0.5091724753477477,
          "coef": 0.5200817253292134,
          "p": 0,
          "se": 0.005562584236987216,
          "t": 93.49642237703854,
          "term": "Intercept"
        },
        {
          "ci_high": 37.58952672831529,
          "ci_low": 36.405046151875126,
          "coef": 36.99728644009521,
          "p": 0,
          "se": 0.30198102503461,
          "t": 122.51526875191895,
          "term": "Income_Below"
        }
      ]
    }

    Digests dos dados

    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

    sha256 do resultado

    b528a65fd574878a3e18b6d30ca750221f44e8e4a0dc81593b785c07631dae03

    Resultado mostrado ao modelo
15. 14Turno do modelo
    call_0d71e147gemini-3.8-flashFim: STOP6.188 ms18366 de entrada · 64 de saída · 426 de raciocínio · 15923 em cache

    Texto visível

    I will run `infer.rdd` on `Support` with `running='Income_Centered'` at cutoff 0.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the `gov_transfers` Dataset and Eligibility** Okay, so my immediate thought is that I need to revisit the `Participation` variable in relation to `Income_Below`. I want to specifically compare the cases where `Income_Below` is less than zero versus when it's greater than zero. To do this, I'm considering using `infer.ols` with subsetting or perhaps `infer.wald_late`. Then, a crucial question arises: does the `gov_transfers` dataset itself contain a direct indicator for eligibility for government transfers? I'll need to inspect the columns of `gov_transfers` using something like `data.describe` to confirm. Looking at the available columns – `rownames`, `Income_Centered`, `Education`, `Age`, `Participation`, `Support`, and `Income_Below` – it seems there isn't an explicit "eligibility" column. This leads me to recall what Huntington-Klein discusses in Chapter 20 of "The Effect," specifically concerning Manacorda, Miguel, and Vigorito (2011). I remember that in their study, eligibility for PANES was determined by an *income eligibility score* derived from household characteristics, not a pre-existing indicator in the dataset. So, back to the `causaldata` package in R and the `gov_transfers` dataset. I need to remember how eligibility is operationalized there. The R code examples from Chapter 20 come to mind. The book states that households with `Income_Centered <= 0` were eligible for PANES. However, I need to be precise: is it *less than or equal to* zero, or strictly *less than* zero? I should verify this. The book also shows examples using `rdrobust` with `y = gov_transfers$Support` and `x = gov_transfers$Income_Centered`, with a cutoff `c = 0`. This strongly suggests that `Income_Centered` is the running variable and `0` is the cutoff for eligibility. Huntington-Klein's use of `Income_Centered` is key here, not `Income_Below`. Therefore, my next step is to consider running `infer.rdd` using `running='Income_Centered'` and `cutoff=0`. I’ll then need to examine what `infer.rdd` returns in this specific configuration to understand the estimated effects based on this eligibility definition.

    Chamadas pedidas

    • infer.rdd

      {
        "cutoff": 0,
        "dataset": "gov_transfers",
        "running": "Income_Centered",
        "y": "Support"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
16. 15Ferramenta
    infer.rddv0.1.0call_ab357d27decidido pelo modeloreexecutável

    Motivo: I will run `infer.rdd` on `Support` with `running='Income_Centered'` at cutoff 0.

    Argumentos

    {
      "bandwidth": null,
      "cluster": null,
      "cutoff": 0,
      "dataset": "gov_transfers",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "Income_Centered",
      "subset": null,
      "y": "Support"
    }

    Figuras call_ab357d27

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Jump in Support at the cutoff of Income_Centered	-0,0166086	-0.01660864823337719	0,04859680.04859678434028409	-0,111857 – 0,0786393

    Diagnósticos

    • bandwidth = 0,008458210.008458207971633918
    • bandwidth_method = "IK"
    • n_left = 473473
    • n_right = 341341
    • kernel = "triangular"
    • polynomial = 11
    • bandwidth_sensitive = false
    • n_obs = 1.9481948
    • se_type = "HC1"

    Tabelas (1)

    {
      "sensitivity": [
        {
          "bandwidth": 0.004229103985816959,
          "ci_high": 0.21293016732705694,
          "ci_low": -0.11843287120008145,
          "estimate": 0.047248648063487754,
          "multiplier": 0.5,
          "n_left": 232,
          "n_right": 149,
          "se": 0.08453294069199428
        },
        {
          "bandwidth": 0.008458207971633918,
          "ci_high": 0.07863929883803972,
          "ci_low": -0.11185659530479408,
          "estimate": -0.01660864823337719,
          "multiplier": 1,
          "n_left": 473,
          "n_right": 341,
          "se": 0.04859678434028409
        },
        {
          "bandwidth": 0.016916415943267836,
          "ci_high": -0.021027509410800366,
          "ci_low": -0.1538774659758384,
          "estimate": -0.08745248769331938,
          "multiplier": 2,
          "n_left": 954,
          "n_right": 675,
          "se": 0.03389091779566909
        }
      ]
    }

    Digests dos dados

    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

    sha256 do resultado

    1e57274f72a1bf9e6869c2d5dc4f3aa742d648e2f214a7b4f2adf1c23e9796eb

    Resultado mostrado ao modelo
17. 16Turno do modelo
    call_5e4325fcgemini-3.8-flashFim: STOP7.289 ms19280 de entrada · 75 de saída · 461 de raciocínio · 15964 em cache

    Texto visível

    I will test `infer.rdd` with the same bandwidth for `Participation` to see the jump in participation.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the RDD Design and Variable Construction** Right, so I've just been examining these results for `running='Income_Centered'` and `running='Income_Below'`, and the coefficients are diametrically opposed: -0.0166086 and +0.0166086 respectively. This immediately flags a dependency in how these variables are defined. The key insight here is the relationship `Income_Below` = -`Income_Centered`. Looking at the provided descriptions, it's clear how this plays out. `Income_Centered` is defined as the household's income minus the eligibility cutoff. Crucially, negative values here mean the household is *below* the cutoff, and thus eligible. Conversely, positive values mean they are above, and ineligible. The description for `Income_Below` states it's "added by the task author: how far below the cutoff the household's income measure is (positive values are eligible)". This is where the magic happens and why the coefficients flip. In Regression Discontinuity (RD) designs, the standard convention for measuring the jump effect at the cutoff is to calculate `f(cutoff^+) - f(cutoff^-)`. This represents the value of the outcome just *above* the cutoff minus the value just *below* it. Now, in the `infer.rdd` implementation, when `Income_Centered` is used, positive values are *ineligible*. This means the "right" side of the cutoff (values greater than 0) corresponds to the *control group*, and the "left" side (values less than 0) corresponds to the *treated group*. Therefore, `f(cutoff^+) - f(cutoff^-)` with `Income_Centered` is effectively measuring Control minus Treated, which explains the negative coefficient if the treatment effect is positive. The task author, wisely, created `Income_Below` to align with the standard RD interpretation. Here, positive values mean eligibility (being *below* the cutoff), so the "right" side of the cutoff (values greater than 0) corresponds to the *treated group*, and the "left" side (values less than 0) corresponds to the *control group*. This means that with `Income_Below`, `f(0^+) - f(0^-)` directly measures Treated minus Control, hence the positive coefficient when there's a positive treatment effect. It's a neat way to standardize the interpretation of the jump. Now, something else caught my eye: the `Participation` variable has this note, "too few observations near the cutoff to estimate the curvature for IK". This suggests a potential issue with the bandwidth selection or the estimation method for curvature. My immediate thought is to explore this further by manually specifying the bandwidth. Specifically, I want to see if I can pass `bandwidth=0.00845821` to `infer.rdd` for `Participation` to see if that resolves the estimation problem or at least gives me more insight into the data's behavior around the cutoff. I need to check if this is a supported parameter for that function call.

    Chamadas pedidas

    • infer.rdd

      {
        "bandwidth": 0.00845821,
        "cutoff": 0,
        "dataset": "gov_transfers",
        "running": "Income_Below",
        "y": "Participation"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
18. 17Ferramenta
    infer.rddv0.1.0call_3ddde208decidido pelo modeloreexecutável

    Motivo: I will test `infer.rdd` with the same bandwidth for `Participation` to see the jump in participation.

    Argumentos

    {
      "bandwidth": 0.00845821,
      "cluster": null,
      "cutoff": 0,
      "dataset": "gov_transfers",
      "kernel": "triangular",
      "level": 0.95,
      "polynomial": 1,
      "running": "Income_Below",
      "subset": null,
      "y": "Participation"
    }

    Figuras call_3ddde208

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Jump in Participation at the cutoff of Income_Below	1	1	0,00000000000000000000000006249376.249371497906592e-26	1 – 1

    Diagnósticos

    • bandwidth = 0,008458210.00845821
    • bandwidth_method = "user"
    • n_left = 341341
    • n_right = 473473
    • kernel = "triangular"
    • polynomial = 11
    • bandwidth_sensitive = false
    • n_obs = 1.9481948
    • se_type = "HC1"

    Tabelas (1)

    {
      "sensitivity": [
        {
          "bandwidth": 0.004229105,
          "ci_high": 1,
          "ci_low": 1,
          "estimate": 1,
          "multiplier": 0.5,
          "n_left": 149,
          "n_right": 232,
          "se": 1.113752173834521e-25
        },
        {
          "bandwidth": 0.00845821,
          "ci_high": 1,
          "ci_low": 1,
          "estimate": 1,
          "multiplier": 1,
          "n_left": 341,
          "n_right": 473,
          "se": 6.249371497906592e-26
        },
        {
          "bandwidth": 0.01691642,
          "ci_high": 1,
          "ci_low": 1,
          "estimate": 1,
          "multiplier": 2,
          "n_left": 675,
          "n_right": 954,
          "se": 9.404586832261106e-26
        }
      ]
    }

    Digests dos dados

    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113

    sha256 do resultado

    a6cc5345a0b249693c2768125226c869b070f64da4f82c58359d77abcfb2437d

    Resultado mostrado ao modelo
19. 18Turno do modelo
    call_d7e0f95egemini-3.8-flashFim: STOP11.892 ms20141 de entrada · 37 de saída · 1323 de raciocínio · 15992 em cache

    Texto visível

    I will load the `reporting-numbers` skill to ensure proper formatting and citation of results.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thoughts on This Regression Discontinuity Analysis** Wow, look at the `Participation` variable! The jump at the cutoff is precisely 1, with a standard error that's practically zero and a confidence interval of [1, 1]. This means every single observation where `Income_Below` was zero or greater participated, and every single observation where `Income_Below` was negative did not. This is a textbook example of a sharp regression discontinuity design – compliance is 100% deterministic. It makes perfect sense now why the IK bandwidth selection for curvature on `Participation` couldn't estimate anything; it's a pure step function with a jump of 1. Consequently, the jump in treatment probability is exactly 1, meaning the intention-to-treat effect and the actual effect of receiving the transfer are identical in this scenario. Now, let's turn to the RDD results for `Support`. In call `call_2a1d3b0a`, with `Income_Below` as the running variable and a cutoff of 0, using the Imbens–Kalyanaraman optimal bandwidth of 0.00845821, a triangular kernel, and a local linear polynomial, I'm seeing a jump in `Support` of 0.0166086. However, the standard error is 0.0485968, and the 95% confidence interval is [-0.0786393, 0.111857]. This means the result isn't statistically significant at the 5% level, as the interval spans zero. The sensitivity analysis for `Support` is particularly interesting. At half the bandwidth (0.0042291), the estimate is -0.0472486. At the full optimal bandwidth, it's 0.0166086. But at *double* the bandwidth (0.0169164), the estimate jumps to 0.0874525 with a standard error of 0.0338909, yielding a confidence interval of [0.0210275, 0.153877]. This doubled bandwidth estimate *is* statistically significant at the 5% level. Let's also consider the McCrary density test (`infer.density_test` with method `mccrary`). The p-value is 0.0815456, which is not statistically significant at the 5% level, suggesting no manipulation of the running variable. This aligns with the "manipulation: false" output. Regarding the choice of polynomial and kernel, the standard practice (as recommended by Gelman and Imbens in 2019) is to use a local linear (polynomial=1) with a triangular kernel, which is what we've used. Higher-order polynomials are generally not advised unless there's a strong theoretical justification. For context, I'm thinking about the original Manacorda et al. (2011) paper, which examined political support both during and after the program. Now, digging deeper into the `Support` outcome in this specific dataset: the mean `Support` is 0.7962. The RD estimate at the optimal bandwidth is about 1.7 percentage points, which is not statistically significant. However, when I look at double the bandwidth, which uses a much larger portion of the sample (practically the entire available sample within the [-0.02, 0.02] range of `Income_Centered`), the estimate becomes statistically significant. The `bandwidth_sensitive` diagnostic being `false` is intriguing because it indicates that estimates at half and double bandwidths fall within the main confidence interval. But the significant finding at double bandwidth, compared to the non-significant finding at the optimal bandwidth, raises questions. The fact that the `gov_transfers` dataset is already restricted to households within 0.02 of the cutoff (`Income_Centered` ranges from -0.019991 to +0.019892) is a crucial detail. This restriction means that even the "doubled" bandwidth is still within a relatively constrained range of the running variable, and the findings across different bandwidths might be sensitive to this local sample selection. I should probably check the `reporting-numbers` skill to ensure my interpretation of these numerical results is sound.

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "reporting-numbers"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
20. 19Habilidade
    reporting-numbersTexto da habilidade
21. 20Turno do modelo
    call_6b9f5eaegemini-3.8-flashFim: STOP17.333 ms21937 de entrada · 647 de saída · 1673 de raciocínio · 19519 em cache

    Texto visível

    I will submit the final answer synthesizing the regression discontinuity analysis and its sensitivity checks.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Government Cash Transfers and Political Goodwill** My primary objective here is to rigorously assess whether government cash transfers, specifically through Uruguay's PANES program, cultivate political goodwill. The central question is whether households nearing the eligibility cutoff for these monthly transfers exhibit increased support for the government. To address this, I employed a sharp Regression Discontinuity Design (RDD). This design is perfectly suited because eligibility for the PANES program was strictly determined by whether a household's measured income fell below a specific cutoff point. I confirmed this strict adherence to the cutoff by observing a deterministic jump in program participation of exactly 1 (`call_3ddde208:effect`) at the eligibility threshold, indicating that households just below the cutoff received the transfer and those just above did not. A crucial diagnostic for any RDD is the McCrary density test, which checks for manipulation or sorting of the running variable (in this case, income) around the cutoff. My analysis of this test (`call_ba2c1148:p_value`) yielded a p-value of 0.0815456. At the conventional 5% significance level, this does not provide statistically significant evidence of manipulation. However, the p-value being close to 0.08 suggests a mild concentration of observations just above the cutoff, which warrants noting as a potential caveat. Turning to the core question of political support, I estimated the local average treatment effect (LATE) of receiving the cash transfer on government support precisely at the eligibility cutoff. Using the Imbens-Kalyanaraman optimal bandwidth of 0.00845820797163392, which utilized 341 observations below and 473 above the cutoff, I found the estimated jump in government support to be 0.0166086 (`call_2a1d3b0a:effect`). This estimate is accompanied by a standard error of 0.0485968 (`call_2a1d3b0a:effect`) and a 95% confidence interval of [-0.0786393, 0.111857]. Interpreting these results, the estimated effect of approximately 0.017 on a 0-to-1 scale for government support is very close to zero and, importantly, is not statistically significant at the 5% level. This means I cannot reject the null hypothesis that there is no immediate change in political support directly at the eligibility threshold. However, it's imperative to consider the sensitivity of these findings to the chosen bandwidth. When I halved the bandwidth, the estimate shifted to -0.0472486. Conversely, when I doubled the bandwidth, the estimated effect increased to 0.0874525, and at this wider bandwidth, the effect becomes statistically significant and positive. This sensitivity indicates that while the effect is not detectable in the immediate vicinity of the cutoff using the optimal bandwidth, there is some evidence of a positive association as we broaden the window. Therefore, my substantive conclusion, based on the optimal bandwidth and local RDD assumptions, is that the PANES program does not demonstrate a statistically significant immediate increase in political goodwill among those who narrowly qualify for the transfers. Nonetheless, the sensitivity analysis highlights a potential positive association at wider bandwidths and the McCrary test suggests a marginal concentration of data that warrants careful consideration. I've ensured that all numerical figures are directly from tool outputs or the original question, properly cited, and that any limitations are clearly articulated as caveats.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Para estimar o efeito do recebimento do benefício do programa PANES sobre o apoio ao governo, emprega-se o desenho de regressão descontínua (RDD). A adesão ao programa salta exatamente em 1 no limiar de elegibilidade (desenho nítido, salto de 1).\n\nCom a largura de banda ótima de Imbens–Kalyanaraman (0,00845821) e ajuste linear local ponderado com núcleo triangular, o salto estimado no apoio ao governo é de 0,0166086 (erro padrão de 0,0485968; IC 95%: [-0,0786393, 0,111857]). O resultado não é estatisticamente significante ao nível de significância usual, indicando que não há evidência de um salto perceptível no apoio político imediatamente em torno da linha de corte de elegibilidade.\n\nO teste de densidade de McCrary na variável de corte resulta em um theta de 0,277737 com p-valor de 0,0815456, não rejeitando a ausência de manipulação no limiar ao nível de 5%. Contudo, a estimativa apresenta sensibilidade à largura de banda: ao reduzir a banda pela metade, o efeito estimado passa a -0,0472486 (IC 95%: [-0,21293, 0,118433]), enquanto ao dobrar a largura de banda, a estimativa eleva-se para 0,0874525 (IC 95%: [0,0210275, 0,153877]).",
        "caveats": [
          "Sensibilidade à largura de banda: a estimativa com a banda ótima é próxima de zero e estatisticamente insignificante (0,0166086), mas atinge 0,0874525 com o dobro da largura de banda, tornando-se positiva e estatisticamente significante nessa janela mais ampla.",
          "Teste de densidade de McCrary: o p-valor de 0,0815456 não indica manipulação ao nível de 5%, mas sugere alguma concentração marginal de observações próxima ao limiar.",
          "Validade local do RDD: o efeito identificado restringe-se estritamente às famílias cujas medidas de renda se situam na vizinhança imediata do ponto de corte de elegibilidade."
        ],
        "figure_refs": [
          "call_3ddde208:effect",
          "call_2a1d3b0a:effect",
          "call_ba2c1148:theta",
          "call_ba2c1148:p_value"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
22. 21Guarda
    resposta aceita

    Números encontrados

    1 · 1 · 0,00845821 · 0,0166086 · 0,0485968 · 95% · -0,0786393 · 0,111857 · 0,277737 · 0,0815456 · 5% · -0,0472486 · 95% · -0,21293 · 0,118433 · 0,0874525 · 95% · 0,0210275 · 0,153877 · 0,0166086 · 0,0874525 · 0,0815456 · 5%

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
23. 22Resposta

    Para estimar o efeito do recebimento do benefício do programa PANES sobre o apoio ao governo, emprega-se o desenho de regressão descontínua (RDD). A adesão ao programa salta exatamente em 1 no limiar de elegibilidade (desenho nítido, salto de 1). Com a largura de banda ótima de Imbens–Kalyanaraman (0,00845821) e ajuste linear local ponderado com núcleo triangular, o salto estimado no apoio ao governo é de 0,0166086 (erro padrão de 0,0485968; IC 95%: [-0,0786393, 0,111857]). O resultado não é estatisticamente significante ao nível de significância usual, indicando que não há evidência de um salto perceptível no apoio político imediatamente em torno da linha de corte de elegibilidade. O teste de densidade de McCrary na variável de corte resulta em um theta de 0,277737 com p-valor de 0,0815456, não rejeitando a ausência de manipulação no limiar ao nível de 5%. Contudo, a estimativa apresenta sensibilidade à largura de banda: ao reduzir a banda pela metade, o efeito estimado passa a -0,0472486 (IC 95%: [-0,21293, 0,118433]), enquanto ao dobrar a largura de banda, a estimativa eleva-se para 0,0874525 (IC 95%: [0,0210275, 0,153877]). Ressalvas: Sensibilidade à largura de banda: a estimativa com a banda ótima é próxima de zero e estatisticamente insignificante (0,0166086), mas atinge 0,0874525 com o dobro da largura de banda, tornando-se positiva e estatisticamente significante nessa janela mais ampla. Teste de densidade de McCrary: o p-valor de 0,0815456 não indica manipulação ao nível de 5%, mas sugere alguma concentração marginal de observações próxima ao limiar. Validade local do RDD: o efeito identificado restringe-se estritamente às famílias cujas medidas de renda se situam na vizinhança imediata do ponto de corte de elegibilidade.

    • Sensibilidade à largura de banda: a estimativa com a banda ótima é próxima de zero e estatisticamente insignificante (0,0166086), mas atinge 0,0874525 com o dobro da largura de banda, tornando-se positiva e estatisticamente significante nessa janela mais ampla.
    • Teste de densidade de McCrary: o p-valor de 0,0815456 não indica manipulação ao nível de 5%, mas sugere alguma concentração marginal de observações próxima ao limiar.
    • Validade local do RDD: o efeito identificado restringe-se estritamente às famílias cujas medidas de renda se situam na vizinhança imediata do ponto de corte de elegibilidade.

    Figuras citadas: call_3ddde208:effect, call_2a1d3b0a:effect, call_ba2c1148:theta, call_ba2c1148:p_value · Parada: answered

    Figura da resposta: call_3ddde208:effect = 11 (se 0,00000000000000000000000006249376.249371497906592e-26)
24. 23Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
25. 24Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_3ddde208:effect (infer.rdd)
26. 25Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.rdd
27. 26Pontuação
    #3argumento igualfalhouobrigatóriainfer.rdd.y = 'Participation'
28. 27Pontuação
    #4argumento igualpassouobrigatóriainfer.rdd.running = 'Income_Below'
29. 28Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.cutoff = 0.0
30. 29Pontuação
    #6argumento igualfalhouregistradainfer.rdd.y = 'Participation'
31. 30Pontuação
    #7argumento igualpassouregistradainfer.rdd.running = 'Income_Below'
32. 31Pontuação
    #8argumento igualpassouregistradainfer.rdd.cutoff = 0.0
33. 32Pontuação
    #9argumento igualfalhouregistradainfer.rdd.kernel = 'triangular'
34. 33Pontuação
    #10argumento igualfalhouregistradainfer.rdd.polynomial = 1
35. 34Pontuação
    #11diagnósticofalhouregistrada341 vs 821
36. 35Pontuação
    #12diagnósticofalhouregistrada473 vs 1127
37. 36Pontuação
    #13figura da resposta próximafalhouregistrada1 vs 0.09285534103880216
38. 37Pontuação
    #14figura da resposta próximafalhouregistrada6.249371498e-26 vs 0.04363357378599472
39. 38Pontuação
    #15figura da resposta próximafalhouregistrada1 vs 0.093
40. 39Pontuação
    #16figura da resposta próximafalhouregistrada6.249371498e-26 vs 0.044

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1falhoupontuada(exibindo)

Downloads desta tarefa