{"attempt":1,"datasets":{"zoorob_does_right":{"description_shown":"This dataset consists of U.S. state-year panel covering 50 states from 1992-2016, combining annual counts of occupational fatalities, employed population, unionization rates, sectoral employment composition, and statutory right-to-work status; each row is a state-year observation with a derived fatality rate per 100,000 workers and its natural logarithm. The fatalities series comes from the Bureau of Labor Statistics' Census of Fatal Occupational Injuries, employment from BLS state employment data, unionization from the Union Membership and Coverage Database, and sectoral composition from BLS industry employment, yielding 1,250 total observations.\n\nVariables:\nstate: State identifier (name or postal abbreviation) for each observation\nyear: Calendar year of the observation, ranging from 1992 to 2016\npolicy: Annual index of state policy liberalism capturing a state's overall regulatory orientation (higher values indicate more liberal policy environments)\nfatalities: Number of occupational fatalities in the state during the year from CFOI (count of deaths)\nemployment: Employed population used to scale fatality counts to rates (number of workers or full-time equivalent workers)\nrate: Occupational fatality rate per 100,000 workers, computed as fatalities divided by employment and multiplied by 100,000\nunion: Unionization rate (share of workers who are union members or covered by a union contract) expressed in percentage points\ntrtraut: Number of workers employed in trade, transportation, and utilities in the state-year (industry employment count)\npctttu: Share of workers employed in trade, transportation, and utilities (percent of total employment)\nmlc: Number of workers employed in mining, logging, and construction in the state-year (industry employment count)\npctmlc: Share of workers employed in mining, logging, and construction (percent of total employment)\nmanuf: Number of workers employed in manufacturing in the state-year (industry employment count)\npctmanufacturing: Share of workers employed in manufacturing (percent of total employment)\nST: State code used for grouping and identification in the dataset (numeric or categorical state code aligned with state)\nRTW: Indicator for whether a right-to-work law is in effect in that state-year (1 = RTW in force, 0 = otherwise)\nlr: Natural logarithm of the fatality rate variable to stabilize scale and interpret changes in proportional terms","file_sha256":"084e55af44aa47af396b25c1a5f50154f0296498873f751c2da4782c2a2f3b18","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"How does the percentage of unionised workers in a state effect the state's occupational fatality rate per 100,000 workers?","question_sha256":"2606e0b28717c4dbd8f242506ac1c388929e33223e80cb86b65eb0be8bfb4453","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":0,"skills":[],"task_id":"causcibench_real.q091","task_sha256":"0fbc76cbe37dca00199ea67eae207f825734d49c4e54a97b347775490bfbda7f","timestamp":"2026-10-15T17:11:44.410120Z"}
{"attempt":1,"attempts":[],"call_id":"call_967f07fc","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":["7148261781b319c73a46c4a9771797398cfb1d5bbd72d395d0c03053c110c553"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"18be80a0aa4543448c2a077e7ee9cb108555dd979ba2f7c0337a3944ebdc061f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","thoughts":null,"tool_calls":[{"args":{"dataset":"zoorob_does_right","x":["union"],"y":"lr"},"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.q091","timestamp":"2026-10-15T17:11:44.410120Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"controls":[],"dataset":"zoorob_does_right","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["union"],"y":"lr"},"attempt":1,"cached":false,"call_id":"call_ed54d46e","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"zoorob_does_right":"981c4493576b3e58caa89f271bf4ae2ef41ab213843ea86787177d7300ceaef0"},"decided_by":"model","duration_ms":14.478500001132488,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"7f9f0db00ae6dcb7d3226094b15ff46f7de6a5fa1118ea9bd07852b2a62492b4","llm_name":"infer__ols","output_digest":"26b6396f62ffbe184c381ffcd4ddb740b17a22c7288f2b3d0fd9630c8ea34f45","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_ed54d46e","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":1250},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.06908146957065542},{"flag":false,"name":"df_resid","note":"","value":1248},{"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":-0.018201438815150586,"ci_level":0.95,"ci_low":-0.02766375908721022,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.022932598951180404,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":1250.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de union na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de lr.","unit":""},{"formula":"ep = \u221aV_jj","result":0.0024115605005975544,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":5.815624048042327e-06}],"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":-0.022932598951180404},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.961866656406711},{"note":"","symbol":"ep","unit":"","value":0.0024115605005975544}],"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 1248 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.union","label":"Coefficient on union in the regression of lr","se":0.0024115605005975544,"unit":"","value":-0.022932598951180404}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":1.865009957123269,"ci_low":1.7546904337616607,"coef":1.8098501954424648,"p":0.0,"se":0.028115958595184527,"t":64.37092263154923,"term":"Intercept"},{"ci_high":-0.018201438815150586,"ci_low":-0.02766375908721022,"coef":-0.022932598951180404,"p":9.473793012011091e-21,"se":0.0024115605005975544,"t":-9.509443758718884,"term":"union"}]},"tool":"infer.ols","version":"0.1.0","warnings":["100 rows were dropped because one of 'lr', 'union' was missing"]},"result_for_model_ref":"288dfb251e3faf98d3f4fd3c6ecba7a9f4a43c0d4191b9e6347111faf6160eed","result_sha256":"934b6e9133893ff2d1b55319e3bd1b64946603525fcae5723f94fa739cb6e92d","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":2,"synthetic":false,"task_id":"causcibench_real.q091","timestamp":"2026-10-15T17:11:44.413112Z","tool_id":"infer.ols","turn_id":"call_967f07fc"}
{"attempt":1,"attempts":[],"call_id":"call_7d1fbf01","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":["7148261781b319c73a46c4a9771797398cfb1d5bbd72d395d0c03053c110c553","4e706ae5484e8d2f9d5d06cc224bfd45ea338549fbcedf991b2ce2727df61cbf","15beaabf8dac226b3d8816d45cb7b905553450a5ecc4a0d8339b030f6c409245"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"18be80a0aa4543448c2a077e7ee9cb108555dd979ba2f7c0337a3944ebdc061f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa ing\u00eanua (infer.ols): coef.union = -0,02293.","caveats":[],"figure_refs":["call_ed54d46e:coef.union"]},"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.q091","timestamp":"2026-10-15T17:11:44.427751Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_32e6aafc","epoch":1,"event":"guard","numbers_found":["-0,02293"],"retries_left":1,"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":4,"task_id":"causcibench_real.q091","timestamp":"2026-10-15T17:11:44.429695Z","turn_id":"call_7d1fbf01","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa ing\u00eanua (infer.ols): coef.union = -0,02293.","answer_figure":{"call_id":"call_ed54d46e","ci_high":-0.018201438815150586,"ci_low":-0.02766375908721022,"formatted":-0.022932598951180404,"key":"coef.union","ref":"call_ed54d46e:coef.union","se":0.0024115605005975544,"tool_id":"infer.ols","unit":"","value":-0.022932598951180404},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_ed54d46e:coef.union"],"run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":5,"stopped":"answered","task_id":"causcibench_real.q091","timestamp":"2026-10-15T17:11:44.429885Z"}
{"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.q091","timestamp":"2026-10-15T17:11:44.429885Z"}
{"attempt":1,"check":{"check_index":1,"detail":"-0.02293259895 vs -0.05","expected":-0.05,"failure_class":"out_of_tolerance","kind":"answer_figure_close","observed":-0.022932598951180404,"passed":false,"required":true,"source_id":"causcibench_real.gold.q091","tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":0.05,"rule":null},"trace_call_id":"call_ed54d46e","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d","seq":7,"task_id":"causcibench_real.q091","timestamp":"2026-10-15T17:11:44.429885Z"}