{"call_id":"call_ea609e6a","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q124/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:02.525292Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"soules_call_to\",\n \"rows\": 232,\n \"description\": \"This dataset is a cross-sectional rebel-group-level file that integrates original measures of recruitment appeals from the Rebel Appeals and Incentives Dataset (RAID) with standardized indicators of rebel ideology, organizational features, and country-level context for 232 rebel movements active worldwide between 1989 and 2011. Each record corresponds to a distinct rebel organization and captures both the breadth and composition of its grievance-based recruitment appeals, as well as its ideological profile, strength, territorial control, ethnic composition, and the regime and economic environment in which it operates.\\n\\nVariable descriptions\\nnew_side_b_id: Numeric group identifier that uniquely references each rebel organization in the merged file, harmonizing the RAID units with the underlying non-state actor and replication identifiers used for statistical analysis.\\nccode: Numeric country code indicating the primary state within which the rebel group is active, enabling linkage to country-level democracy and GDP covariates and the clustering of standard errors by conflict country.\\nideol_ord: Ordinal indicator capturing the group’s core ideology type as used in the models (e.g., distinguishing radical Islamist from other ideological families) to operationalize the main explanatory contrast in the paper.\\nother_rel_appeals: Dichotomous RAID-based indicator coded 1 if the group sometimes employs recruitment appeals to non-Islamist religious ideologies (e.g., generic religious or non-radical faith-based claims), and 0 otherwise.\\nleft_appeals: Binary variable denoting whether the group recruits using left-wing or communist ideological appeals, such as references to Marxist principles or anti-capitalist class struggle.\\nanti_resource_appeals: Indicator coded 1 when rebel recruitment materials highlight opposition to the exploitation of local resources by external actors, including grievances about plunder or unequal resource extraction.\\ngov_violence_appeals: Binary measure for appeals centered on violent government repression and human rights abuses, where recruitment narratives emphasize state-perpetrated killings, torture, or indiscriminate attacks.\\nfeminist_appeals: Indicator for appeals invoking women’s rights or gender inequality as a recruitment theme, such as mobilizing against gendered exclusion or violence.\\nother_appeals: Catch-all binary category capturing miscellaneous recruitment appeals that do not fit the primary ideological or grievance types but nonetheless appear as recurring frames in the group’s propaganda.\\nethnic_nat_appeals: Dichotomous RAID measure indicating whether the group mobilizes recruits using ethnonationalist or regional grievances, such as discrimination against a specific ethnic community or demands for national self-determination.\\nanti_intervention_appeals: Binary indicator coded 1 when groups employ recruitment appeals opposing foreign military intervention or occupation, including denunciations of external troops or international coalitions.\\ngov_grievance_appeals: Variable marking general anti-government grievance appeals, where recruitment emphasizes corruption, misrule, or broad political injustices without being limited to a single policy issue.\\nmulti_ethnic: Dichotomous measure from RAID indicating whether the group’s membership is multi-ethnic, capturing heterogeneity in the ethnic background of recruits rather than whether ethnicity is the explicit basis of mobilization.\\nsecessionist: Binary indicator of whether the organization has secessionist aims, that is, whether it seeks to create an independent state separate from the existing central government.\\nreb_strength_ord: Ordinal measure of relative rebel strength taken from the NSA dataset, capturing expert assessments of whether the group is weaker, roughly equal, or stronger relative to government forces.\\nname: Text string with the full or commonly used name of the rebel organization, preserving interpretable labels alongside numeric identifiers for substantive interpretation and replication.\\nmulti_appeals: Binary summary indicator coded 1 if the group uses two or more distinct recruitment appeals (across the RAID dimensions), operationalizing whether recruitment is diversified versus narrow.\\nterr_cont: Dichotomous measure for whether the group controls territory at any point, indicating de facto governance or sustained physical control of areas as captured in the NSA data.\\nany_nr_binary: Indicator for whether the group exploited any natural resources (e.g., lootable or extractive commodities) during its life, derived from the Rebel Contraband Dataset and collapsed into a yes/no measure.\\nappeals_count: Count variable measuring the total number of distinct recruitment appeals coded for the group across the RAID dimensions, ranging from 0 (no grievance-based appeals, only material incentives) to higher values for groups with more diverse appeals.\\nother_group_binary: Binary measure capturing whether another rebel group was present in the same country when this group entered the armed conflict dataset, providing a simple indicator of inter-rebel competition.\\nleft_wing: Dichotomous indicator (from WARD/FORGE) for whether the organization has a communist or broader left-wing founding ideology, used both as a control and for constructing alternative measures of ideological diversity.\\nsupporttype_funds_rebel: Binary variable indicating whether the group received financial support from external actors (e.g., state sponsors or foreign patrons), based on external support data on rebel funding.\\nradical_islamist: Core binary explanatory variable coded 1 for groups with a radical Islamist ideology—those espousing goals such as establishing an Islamic state, enforcing religious law, or defending Islam—and 0 otherwise.\\nforeign_f: Dichotomous indicator of whether the group recruits foreign fighters, based on external foreign-fighter datasets and used to test how internationalization of recruitment conditions appeal diversity.\\nv2x_polyarchy: Continuous country-level democracy score (polyarchy index) for the group’s main country, capturing the extent of electoral competition and participation and serving as a control for regime type.\\nmean_log10_gdp_pc: Continuous measure of average logged GDP per capita in the group’s host country over its active period, capturing income levels and included as a macroeconomic control.\\ngroup_age: Numeric variable measuring the age or duration of the group (e.g., years active), used as a control to account for the possibility that older organizations have had more time to diversify appeals.\\nideology_forge_count: Count of the number of distinct founding ideologies attributed to the group in the FORGE dataset (e.g., religious, nationalist, left-wing), used in robustness checks on broader ideological diversity.\\nincompatible_appeals: Binary measure constructed by examining all pairwise correlations among appeals in RAID and coding 1 if the group employs at least one pair of appeals that are negatively and significantly associated in the full sample, indicating that it combines appeals most groups avoid mixing.\\npost_2003: Dichotomous indicator taking value 1 for groups active after 2003, capturing the post–Iraq invasion period in which transnational jihadist dynamics and perceptions of threats to Islam intensified.\\nideology_count: Count variable summarizing the number of ideologies a group represents across external ideology datasets (e.g., socialism, Islamism, nationalism), used in robustness checks on ideological diversification beyond recruitment appeals.\\nappeals_count_restrict: Alternative, more restrictive count of recruitment appeals that excludes appeals corresponding to the group’s parent ideology (e.g., omitting Islamist appeals for Islamist groups), used to test whether results hold when focusing on “non-core” grievances.\\nmulti_appeals_restrict: Binary version of the restrictive count, coded 1 if the group makes two or more non-parent appeals, allowing analyses of diversified grievance framing net of primary ideology.\\nyear_start: Calendar year in which the group first appears in the armed conflict or non-state actor data, anchoring the onset of its conflict activity and the period over which contextual covariates are averaged or coded.\\nyear_end: Calendar year in which the group exits the dataset (e.g., due to dissolution, defeat, or end of recorded activity), marking the end of its observed life span for the purposes of computing group age and averaging country-level variables.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"new_side_b_id\": \"int64\",\n  \"ccode\": \"int64\",\n  \"ideol_ord\": \"int64\",\n  \"other_rel_appeals\": \"float64\",\n  \"left_appeals\": \"float64\",\n  \"anti_resource_appeals\": \"float64\",\n  \"gov_violence_appeals\": \"float64\",\n  \"feminist_appeals\": \"float64\",\n  \"other_appeals\": \"float64\",\n  \"ethnic_nat_appeals\": \"float64\",\n  \"anti_intervention_appeals\": \"float64\",\n  \"gov_grievance_appeals\": \"float64\",\n  \"multi_ethnic\": \"float64\",\n  \"secessionist\": \"int64\",\n  \"reb_strength_ord\": \"int64\",\n  \"name\": \"str\",\n  \"multi_appeals\": \"int64\",\n  \"terr_cont\": \"float64\",\n  \"any_nr_binary\": \"float64\",\n  \"appeals_count\": \"int64\",\n  \"other_group_binary\": \"int64\",\n  \"left_wing\": \"int64\",\n  \"supporttype_funds_rebel\": \"float64\",\n  \"radical_islamist\": \"int64\",\n  \"foreign_f\": \"float64\",\n  \"v2x_polyarchy\": \"float64\",\n  \"mean_log10_gdp_pc\": \"float64\",\n  \"group_age\": \"int64\",\n  \"ideology_forge_count\": \"float64\",\n  \"incompatible_appeals\": \"int64\",\n  \"post_2003\": \"int64\",\n  \"ideology_count\": \"int64\",\n  \"appeals_count_restrict\": \"float64\",\n  \"multi_appeals_restrict\": \"float64\",\n  \"year_start\": \"float64\",\n  \"year_end\": \"float64\"\n }\n}\n\n# Question\nDoes being a radical Islamist group causally increase the number of recruitment appeals they make?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"soules_call_to\", \"y\": \"appeals_count\", \"x\": [\"radical_islamist\"], \"controls\": [\"secessionist\", \"left_wing\", \"multi_ethnic\", \"any_nr_binary\", 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{"call_id":"call_b6f754bb","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q124/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"soules_call_to","y":"appeals_count","x":["radical_islamist"],"controls":["secessionist","left_wing","multi_ethnic","any_nr_binary","supporttype_funds_rebel","group_age","reb_strength_ord","terr_cont","other_group_binary","post_2003","v2x_polyarchy","mean_log10_gdp_pc"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"c2cc2e196123c02639d72c8016c8a9855ea3e28f42f1e03ec1a289bd8a66e23a","dataset_digests":{"soules_call_to":"ecaf23cddc67125147defa7c08b7937c8fdf1e087f3c956da1f5a27cefb1e00c"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b6f754bb","status":"ok","reason":"","figures":[{"key":"coef.radical_islamist","value":0.7832864665022266,"unit":"","label":"Coefficient on radical_islamist in the regression of appeals_count","se":0.16835427253644383,"ci_low":0.4512785353860737,"ci_high":1.1152943976183793,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de radical_islamist na regressão por mínimos quadrados ordinários de appeals_count.","formula":"","terms":[{"symbol":"n","value":211.0,"unit":"observações","note":""},{"symbol":"k","value":14.0,"unit":"parâmetros","note":""}],"result":0.7832864665022266,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.028343161081275207,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.16835427253644383,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 197 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · 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clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.9799028124535432,"se":0.24907768509967165,"t":3.9341252591994436,"p":0.00011567589821454359,"ci_low":0.48870193188639655,"ci_high":1.4711036930206898},{"term":"radical_islamist","coef":0.7832864665022266,"se":0.16835427253644383,"t":4.652608185709499,"p":6.005709291356233e-6,"ci_low":0.4512785353860737,"ci_high":1.1152943976183793},{"term":"secessionist","coef":-0.12952025798140324,"se":0.16935876862096155,"t":-0.7647685386239429,"p":0.44532452214776536,"ci_low":-0.46350913476534605,"ci_high":0.2044686188025396},{"term":"left_wing","coef":0.2089942748959342,"se":0.17263861168959893,"t":1.2105882505108538,"p":0.22750366109301096,"ci_low":-0.13146271163775214,"ci_high":0.5494512614296205},{"term":"multi_ethnic","coef":-0.18014432850044304,"se":0.13030456165378412,"t":-1.3824867388686046,"p":0.16838756127698357,"ci_low":-0.43711522254356894,"ci_high":0.07682656554268286},{"term":"any_nr_binary","coef":-0.0807388091694087,"se":0.13855074832488076,"t":-0.5827381673903939,"p":0.5607359931726689,"ci_low":-0.353971835055228,"ci_high":0.19249421671641062},{"term":"supporttype_funds_rebel","coef":0.058648113626436536,"se":0.17898553690735639,"t":0.3276695683897243,"p":0.7435094721452239,"ci_low":-0.29432551105814936,"ci_high":0.4116217383110224},{"term":"group_age","coef":0.01586164320388359,"se":0.006481003808298171,"t":2.4474053206965563,"p":0.01526503920105231,"ci_low":0.003080591475700136,"ci_high":0.02864269493206704},{"term":"reb_strength_ord","coef":0.15816941619129143,"se":0.09087583244014046,"t":1.7405003282415816,"p":0.08333264079787261,"ci_low":-0.02104490764107786,"ci_high":0.3373837400236607},{"term":"terr_cont","coef":0.1065858173339672,"se":0.15722734449115333,"t":0.6779089075053636,"p":0.49862488839501395,"ci_low":-0.20347893227370623,"ci_high":0.41665056694164065},{"term":"other_group_binary","coef":-0.02704066931945161,"se":0.12803284013780003,"t":-0.21120104256336186,"p":0.8329487154494198,"ci_low":-0.2795315489903218,"ci_high":0.22545021035141857},{"term":"post_2003","coef":-0.05304048685140607,"se":0.14346223194223046,"t":-0.36971742411455355,"p":0.7119901796909648,"ci_low":-0.3359593466037473,"ci_high":0.2298783729009352},{"term":"v2x_polyarchy","coef":0.9940905354195498,"se":0.3562179556724445,"t":2.7906805920071376,"p":0.005776922586880778,"ci_low":0.29160057358248204,"ci_high":1.6965804972566176},{"term":"mean_log10_gdp_pc","coef":-0.10322349669655463,"se":0.1736617650971492,"t":-0.5943939164663548,"p":0.5529306841598718,"ci_low":-0.4456982226136099,"ci_high":0.23925122922050068}]},"datasets":{},"warnings":["21 rows were dropped because one of 'appeals_count', 'radical_islamist', 'secessionist', 'left_wing', 'multi_ethnic', 'any_nr_binary', 'supporttype_funds_rebel', 'group_age', 'reb_strength_ord', 'terr_cont', 'other_group_binary', 'post_2003', 'v2x_polyarchy', 'mean_log10_gdp_pc' was missing"],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"9d83d62ab861a3deed51f4a81e66da95ad22c8d80a5a14e5f02adf18d2dcf894","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:02.529300Z","duration_ms":3.9855418726801872,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: 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