{"attempt":1,"datasets":{"leigh_estimating_the_impact":{"description_shown":"The dataset is a state-year panel spanning mid-20th century to early-21st century state politics and economics, combining election results, legislative composition, fiscal policy, labor markets, distributional statistics, crime, education, and demographics for each state and year.\n\nThe variables are:\nstatename: State name string identifier for observations and merges across sources\nstcode: Two-letter state postal abbreviation used as a compact state identifier\nstfips: Numeric state FIPS code used for merges and fixed effects\nyear: Calendar year aligning outcomes, political control, and policy variables within each state\nstatename/stcode/stfips/year naming: Core keys establishing a state-year panel structure spanning mid-20th to early-21st centuries\nhouse_dems: Number of Democratic seats in the state lower (house) chamber in a given year\nhouse_total: Total seats in the state lower chamber in a given year\nhouse_pctdem: Democratic seat share in the lower chamber, house_dems/house_total\nhouse_margin: Lower-chamber seat margin (e.g., Democratic minus Republican seats)\nhouse_cntrl: Lower-chamber party control indicator based on majority\nsenate_dems: Number of Democratic seats in the state upper (senate) chamber in a given year\nsenate_total: Total seats in the state upper chamber in a given year\nsenate_pctdem: Democratic seat share in the upper chamber, senate_dems/senate_total\nsenate_margin: Upper-chamber seat margin (e.g., Democratic minus Republican seats)\nsenate_cntrl: Upper-chamber party control indicator based on majority\ntotal_dems: Total Democratic seats across both chambers in a given year\ntotal_pctdem: Democratic seat share across both chambers in a given year\ntotal_margin: Combined legislative seat margin across both chambers\ntotal_cntrl: Overall legislature party control indicator based on combined chamber majorities\ndivided: Indicator that governor's party and legislative control differ in a state-year\nhouse_switch: Indicator that lower-chamber party control changed relative to prior year\nsenate_switch: Indicator that upper-chamber party control changed relative to prior year\ngovernor: Party of the sitting governor in the state-year (categorical or binary as coded)\ngov_dem/gov_dem2/gov_dem_p: Alternative codings or interactions of a Democratic-governor indicator used for robustness or subgrouping as defined in the data\ntermyear/sterm/term: Indicators for gubernatorial term timing or tenure alignment with outcomes as defined in the data\ndem_voteshare/voteshare: Two-party Democratic vote share in the most recent gubernatorial election retained for political context\npoverty_pct: Share of persons below the official poverty line in a state-year\npovertylineratio: Income-to-poverty-line ratio measure summarizing depth of poverty in a state-year\nminwage/fminwage/sminwage: Effective, federal, and state minimum wage levels operative in a state-year\ncpiy: Consumer price index or price deflator used for inflation adjustment of nominal series\npop_noninstitutional: Non-institutional civilian population used as labor-market denominator\nlaborforce: Civilian labor force level (employed plus unemployed) in a state-year\nemployed: Number of employed persons in a state-year\nunemployed: Number of unemployed persons in a state-year\nunemploymentrate: Unemployment rate (unemployed/laborforce) in a state-year\nemployment: Total nonfarm employment level in a state-year\nlnemp: Natural logarithm of employment for elasticity-based specifications\nemploymentgap: Deviation of employment from a benchmark or reference as defined in the data\ngsp: Gross state product in current or chained dollars as defined in the series\nemployeecomp: Compensation of employees aggregate (wages and supplements) at the state level\npop/lnpop: Total resident population and its natural logarithm for scale adjustments\npersinc_percap/disppersinc_percap: Personal and disposable personal income per capita in a state-year\npropertyinc: Property income (dividends, interest, rent) aggregate at the state level\neitc_s_child1: State earned income tax credit generosity (e.g., percent of federal) for first child where applicable\nmaxbenefit/afdc_max: Maximum cash-assistance benefit (e.g., AFDC/TANF) for a reference family\nwelfarereform: Indicator for major state welfare reform adoption in a state-year\nafdcwaiver: Indicator for AFDC/TANF policy waivers in a state-year\nafdc_reca: AFDC/TANF recipiency or caseload indicator in a state-year\ntransfers/ui/famassist/foodstamps: Transfers to persons and components (unemployment insurance, family assistance, SNAP)\nstinctax/stmotor/stothertax/stnontaxes: State individual income tax, motor-related taxes, other taxes, and non-tax revenues in a state-year\nstrevenue/strevenue_inc/stinctax_inc/stothertax_inc: Total state revenue and year-over-year revenue changes overall and by tax category\nstax_corpr: State corporate income tax rate or effective measure in a state-year\nstax_topr/stax_botr: Top and bottom marginal personal income tax rates in a state-year\nstax_topb/stax_botb: Taxable income thresholds for top and bottom personal income tax brackets in a state-year\natr: Average tax rate or analogous aggregate rate measure as defined in the tax series\nincrate: Incarceration rate per 100,000 population in a state-year\nvcrime/pcrime: Violent and property crime rates per 100,000 population in a state-year\nexecutions: Number of executions carried out in the state-year\nmrate: All-cause mortality rate per 100,000 population in a state-year\nfsuicide/msuicide/psuicide: Female, male, and total suicide rates per 100,000 population in a state-year\nnaep_read: NAEP reading assessment metric for the state-year\nunionization: Union membership or density measure at the state-year level\ngovpower/govbpower/govpower_std: Indices of gubernatorial institutional power and standardized variants used to study moderation by executive authority\nregion: Census region categorical identifier used in grouped analyses and controls\nregionyr1-regionyr4 naming: Region-by-year interaction indicators (categories 1-4) capturing differential regional trends over time\nprscore/prscored/prscorer: Policy orientation indices summarizing policy stance and partisan policy bundles as defined in the series\nredist: Redistribution index summarizing post-fiscal distributional change in a state-year\nstempwages/stemploy/stempavw: State government employee wage bill or average wages and state-government employment levels\nhouse_dem/senate_dem/leg_dem/leg_rep: Indicators for Democratic or Republican legislative control by chamber or overall legislature\nlegterm/legtermyear: Legislative term-related indicators aligning outcomes with legislative cycles as defined in the data\nall_dem/all_gop: Indicators for unified Democratic or Republican control across relevant state institutions\nall_dem_pd/all_gop_pg: Auxiliary unified control indicators combining party with dataset-specific dimensions as defined in the series\npres_dem/pres_dem2/fsen_dem/fhouse_dem/gov_pres_dem: Federal partisan context indicators (presidential party, congressional party, and governor-president alignment)\nfdem1/fdem2/dem1: Auxiliary Democratic indicators used in pooled or robustness specifications as defined in the series\ngini/gini_census_family: Gini coefficients for pre-tax/transfer income (household/family) and census-family series at the state-year level\n_9050/_5010/_9010/_7525 naming: Pre-tax percentile ratios where _ppqq denotes p-th to q-th percentile income ratio (e.g., _9010 = 90th/10th; _9050 = 90th/50th; _5010 = 50th/10th; _7525 = 75th/25th)\n_9050_pt/_5010_pt/_9010_pt/_7525_pt naming: Post-tax/transfer counterparts to percentile ratios with _pt suffix indicating post-fiscal distributions\nge_m1/ge_0/ge_1/ge_2 naming: Generalized Entropy indices with parameter set k in {-1,0,1,2} for pre-tax distributions, where k indexes sensitivity to different parts of the income distribution\nge_m1_pt/ge_0_pt/ge_1_pt/ge_2_pt naming: Post-tax/transfer counterparts to Generalized Entropy indices with _pt suffix indicating post-fiscal distributions\natk_05/atk_1/atk_2 naming: Atkinson inequality indices with inequality-aversion parameter epsilon in {0.5,1,2} for pre-tax distributions\natk_05_pt/atk_1_pt/atk_2_pt naming: Post-tax/transfer counterparts to Atkinson indices with _pt suffix indicating post-fiscal distributions\nfaminc10-faminc90 naming: Pre-tax family income at selected percentiles where faminc## gives the ##th percentile (## in {10,20,...,90}) in nominal or real terms as defined in the series\nfamincpt10-famincpt90 naming: Post-tax/transfer family income at selected percentiles where famincpt## gives the ##th percentile (## in {10,20,...,90})\nmover10-mover90 naming: Family income at selected percentiles among movers where mover## gives the ##th percentile for movers (## in {10,20,...,90})\nnonmover10-nonmover90 naming: Family income at selected percentiles among non-movers where nonmover## gives the ##th percentile for non-movers (## in {10,20,...,90})\nmean_faminc/sd/cov: Mean, standard deviation, and coefficient of variation of pre-tax family income distribution in a state-year\nmean_faminc_pt/sd_pt/cov_pt: Mean, standard deviation, and coefficient of variation of post-tax/transfer family income distribution in a state-year\ngini_nat/gini_nat_white/gini_nat_black/gini_nat_hispanic: National Gini benchmarks overall and by race used for normalization and comparative context\nnat##th/nat##th_real naming: National ##th percentile income thresholds (## in {20,40,60,80,95}) in nominal (nat##th) and real (nat##th_real) terms for contextual comparison\nnum_families: Number of families used in constructing the state-level distributional statistics\nlinetitle: Line-item descriptor labeling fiscal series entries for reconciliation and documentation\nIstcode##: State fixed-effect dummies where Istcode## denotes a specific state category (## indexes state codes) to absorb time-invariant state heterogeneity\nIyear_####: Year fixed-effect dummies where _Iyear_#### denotes a calendar year indicator to absorb common shocks and national trends","file_sha256":"29dc7a900c78bf24b67811856cb3d85d5759a0288211332d090607e89b6e451d","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"What is the effect of having a Democratic governor on a state's unionization rate?","question_sha256":"41717e4eebfe3f76114b2dcca0be8648d979188fd4fdb8a58fc74ecc9b385375","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":0,"skills":[],"task_id":"causcibench_real.q047","task_sha256":"fc168c191fa8a6fa13f29e6a52fc0b42b5468e28ee305ace8ef83be2a306a1e1","timestamp":"2026-10-15T17:11:44.423708Z"}
{"attempt":1,"attempts":[],"call_id":"call_e1144f7b","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["282bf609feddd8879a572f8a3299892dde6f650bcdb3aae20406e649271a0034"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b67091a59930054c191a615a2a357d840e8ab097d25692ec84f1d2b9c231ec26","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","thoughts":null,"tool_calls":[{"args":{"dataset":"leigh_estimating_the_impact","x":["gov_dem"],"y":"unionization"},"llm_name":"infer__ols","tool_id":"infer.ols","vendor_id":"script_1"}]},"response_id":null,"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":1,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.423708Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"controls":[],"dataset":"leigh_estimating_the_impact","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["gov_dem"],"y":"unionization"},"attempt":1,"cached":false,"call_id":"call_c3f11244","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"leigh_estimating_the_impact":"f91ff21282f3cde7127c7cb09e0a44d54eb40d66c98188a5cf7a3a31329e3b6e"},"decided_by":"model","duration_ms":5.386207951232791,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"c5ce8b12c7ee31582e254eab7436a5b9c688c30e078bcef6c29e54a157020f23","llm_name":"infer__ols","output_digest":"94a25f2f7ef8ec9d0c531e082a4c8387b0d73888327ebc44c49094aa9f787efb","rationale":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_c3f11244","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":1901},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.0009251315324216858},{"flag":false,"name":"df_resid","note":"","value":1899},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":0.002645748085196976,"ci_level":0.95,"ci_low":-0.013164160572909518,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.005259206243856271,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":1901.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de gov_dem na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de unionization.","unit":""},{"formula":"ep = \u221aV_jj","result":0.004030643455462887,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":1.62460866650658e-05}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":-0.005259206243856271},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9612139888829305},{"note":"","symbol":"ep","unit":"","value":0.004030643455462887}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 1899 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.gov_dem","label":"Coefficient on gov_dem in the regression of unionization","se":0.004030643455462887,"unit":"","value":-0.005259206243856271}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":0.19574528601520033,"ci_low":0.18322412562950555,"coef":0.18948470582235294,"p":0.0,"se":0.0031921963785365914,"t":59.358724637492074,"term":"Intercept"},{"ci_high":0.002645748085196976,"ci_low":-0.013164160572909518,"coef":-0.005259206243856271,"p":0.19211713700128585,"se":0.004030643455462887,"t":-1.3048056227172031,"term":"gov_dem"}]},"tool":"infer.ols","version":"0.1.0","warnings":["4837 rows were dropped because one of 'unionization', 'gov_dem' was missing"]},"result_for_model_ref":"49e994d33b19124cc191da791121752bcaadfab9738ab309f474b3ed7af93c98","result_sha256":"7c3adc8204bc7cd5ca921942da33f0bb0267d1df368092e4a08b398277fc4001","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":2,"synthetic":false,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.429342Z","tool_id":"infer.ols","turn_id":"call_e1144f7b"}
{"attempt":1,"attempts":[],"call_id":"call_5e15125a","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["282bf609feddd8879a572f8a3299892dde6f650bcdb3aae20406e649271a0034","854e183893b9cc086aca1f7af14c812ed6e31185998e8e5672dd3bc2bdd9568c","0232d4328280e8ae5998b943fbb93e7477e160860af23e6f0a7146a5a50f387a"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b67091a59930054c191a615a2a357d840e8ab097d25692ec84f1d2b9c231ec26","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa ing\u00eanua (infer.ols): coef.gov_dem = -0,005259.","caveats":[],"figure_refs":["call_c3f11244:coef.gov_dem"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"script_2"}]},"response_id":null,"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":3,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.434924Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_e09a3a70","epoch":1,"event":"guard","numbers_found":["-0,005259"],"retries_left":1,"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":4,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.436697Z","turn_id":"call_5e15125a","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa ing\u00eanua (infer.ols): coef.gov_dem = -0,005259.","answer_figure":{"call_id":"call_c3f11244","ci_high":0.002645748085196976,"ci_low":-0.013164160572909518,"formatted":-0.005259206243856271,"key":"coef.gov_dem","ref":"call_c3f11244:coef.gov_dem","se":0.004030643455462887,"tool_id":"infer.ols","unit":"","value":-0.005259206243856271},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_c3f11244:coef.gov_dem"],"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":5,"stopped":"answered","task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.436887Z"}
{"attempt":1,"check":{"check_index":0,"detail":"every number backed","expected":null,"failure_class":null,"kind":"answer_backed","observed":[],"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":6,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.436887Z"}
{"attempt":1,"check":{"check_index":1,"detail":"-0.005259206244 vs -0.277","expected":-0.277,"failure_class":"out_of_tolerance","kind":"answer_figure_close","observed":-0.005259206243856271,"passed":false,"required":true,"source_id":"causcibench_real.gold.q047","tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":0.05,"rule":null},"trace_call_id":"call_c3f11244","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":7,"task_id":"causcibench_real.q047","timestamp":"2026-10-15T17:11:44.436887Z"}