{"attempt":1,"datasets":{"hartford_deepiv":{"description_shown":"This dataset contains simulated consumer behavior data examining relationships between pricing decisions and market demand across various emotional and temporal contexts. The data structure reflects a complex economic environment where pricing strategies may be influenced by factors that also affect consumer demand. Pricing information is represented through both continuous normalized measures and categorical indicators distinguishing between different price levels. The data captures market demand responses across various pricing scenarios, with additional variables tracking price positioning within broader market distributions.\n\nVariable description:\nobs_id: unique observation identifier\nseed: randomization seed for simulation\ntreatment_price_normalized: standardized pricing measure\noutcome_demand: consumer demand response\ninstrument_z: auxiliary variable for pricing analysis\ntemporal_time: time measurement\nstate_emotion_id: emotional state category identifier; takes values 0, 1, 2, ..., 6\ncovariate_emotion_0 to covariate_emotion_6: emotional state indicators across seven categories\nunobserved_confounder_v: unmeasured factor affecting pricing and demand\nprice_percentile: pricing position within market distribution\ntreatment_high_price: indicator for high pricing condition\ntreatment_low_price: indicator for low pricing condition\ninstrument_strong: strength indicator for auxiliary pricing variable\ntime_category: categorical time period\ncounterfactual_demand_low_price: hypothetical demand under low pricing\ncounterfactual_demand_high_price: hypothetical demand under high pricing\nindividual_treatment_effect: person-specific response to pricing changes","file_sha256":"0aabfb5579727e6c1094a3e837924248194f9fbf32e9ed8a7db093a4f0dd632d","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"How does increase in product cost affect customer demand?","question_sha256":"3f8b61542ddf71ced83f38eb537387e6b2f2d1c147e9bf8d29fe498f1f6707cb","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":0,"skills":[],"task_id":"causcibench_real.q153","task_sha256":"4a02ec509d8169ee7cd254d9e2cdca311a6cc92b6fd26affb826829da7c8b52b","timestamp":"2026-10-15T17:11:03.974436Z"}
{"attempt":1,"attempts":[],"call_id":"call_ae59e032","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"reference","model_requested":"reference","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["871733d7a01e2f911192b7cae3b95f08c3cc79ec7026220c7a4f8d34180ea98e"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a2be08c22418e5f99d74884496b58623f829b1cd34e8445acc19fc3a8299d1ac","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"causcibench_metadata_rule/1: iv","thoughts":null,"tool_calls":[{"args":{"dataset":"hartford_deepiv","endog":["treatment_price_normalized"],"exog":["covariate_emotion_0","covariate_emotion_1","covariate_emotion_2","covariate_emotion_3","covariate_emotion_4","covariate_emotion_5","covariate_emotion_6","temporal_time"],"instruments":["instrument_z"],"y":"outcome_demand"},"llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","vendor_id":"script_1"}]},"response_id":null,"run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":1,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.974436Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"dataset":"hartford_deepiv","endog":["treatment_price_normalized"],"exog":["covariate_emotion_0","covariate_emotion_1","covariate_emotion_2","covariate_emotion_3","covariate_emotion_4","covariate_emotion_5","covariate_emotion_6","temporal_time"],"instruments":["instrument_z"],"level":0.95,"se":"HC1","subset":null,"y":"outcome_demand"},"attempt":1,"cached":false,"call_id":"call_ba7ec5d1","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"hartford_deepiv":"83b4298ec803f1d6cfa4e643ce2340ea68a0ce366c6dc706ad7d6753904a0b69"},"decided_by":"model","duration_ms":14.991499949246645,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"7c89d3bfe09c2598c3f367eb42a14089614b33e79bc8a1944d97ba9042591d3c","llm_name":"infer__iv_2sls","output_digest":"9c8defbb7e0d632a1d888a007c2553003e6f9e0c08ad59a5f8ed3ac76cc0507d","rationale":"causcibench_metadata_rule/1: iv","replay_class":"replayed","result":{"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; their variance may differ"],"call_id":"call_ba7ec5d1","datasets":{},"diagnostics":[{"flag":false,"name":"first_stage_F.treatment_price_normalized","note":"Wald F of the excluded instruments in the first stage for treatment_price_normalized (heteroskedasticity-robust standard errors (HC1))","value":827.8099469518463},{"flag":false,"name":"partial_r2.treatment_price_normalized","note":"share of the variation the instruments explain beyond the controls","value":0.5183650008830992},{"flag":false,"name":"weak_instrument","note":"every first-stage F is at least 10","value":false},{"flag":false,"name":"n_obs","note":"","value":1000},{"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":-0.22002343381270065,"ci_level":0.95,"ci_low":-0.45612151163599346,"derivation":{"note_pt":"","steps":[{"formula":"\u03b2 = \u03c1 / \u03c0","result":-0.33807247272434704,"terms":[{"note":"forma reduzida","symbol":"\u03c1","unit":"","value":0.21262185422812374},{"note":"primeiro est\u00e1gio","symbol":"\u03c0","unit":"","value":-0.6289238887588712}],"text_pt":"Com um instrumento e um regressor end\u00f3geno, o MQ2E \u00e9 a raz\u00e3o entre o coeficiente de instrument_z na forma reduzida (outcome_demand sobre instrument_z e os controles) e no primeiro est\u00e1gio (treatment_price_normalized sobre instrument_z e os controles).","unit":""},{"formula":"ep = \u221aV_jj","result":0.060156647201061696,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.003618822202473004}],"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":-0.33807247272434704},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.962360676736036},{"note":"","symbol":"ep","unit":"","value":0.060156647201061696}],"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 991 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.treatment_price_normalized","label":"2SLS effect of treatment_price_normalized on outcome_demand","se":0.060156647201061696,"unit":"","value":-0.33807247272434704}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":-1.9637585172966516,"ci_low":-2.470573800944057,"coef":-2.2171661591203544,"p":4.8158951951294114e-58,"se":0.1291340806141671,"t":-17.16948886440682,"term":"Intercept"},{"ci_high":2.9416734262000723,"ci_low":2.389482942184654,"coef":2.665578184192363,"p":1.4921457569868447e-68,"se":0.14069546199169372,"t":18.945729638030055,"term":"covariate_emotion_0"},{"ci_high":2.610488225745559,"ci_low":2.0720140093097634,"coef":2.341251117527661,"p":1.9384848599882037e-57,"se":0.1372006234173606,"t":17.064434980048436,"term":"covariate_emotion_1"},{"ci_high":1.8970633628201352,"ci_low":1.345128998181844,"coef":1.6210961805009896,"p":6.122686032307848e-29,"se":0.14063020401435966,"t":11.527368475803822,"term":"covariate_emotion_2"},{"ci_high":1.537272336325403,"ci_low":0.9765690448135562,"coef":1.2569206905694796,"p":6.085061178490278e-18,"se":0.1428644841284875,"t":8.797992714823703,"term":"covariate_emotion_3"},{"ci_high":1.2617306605726306,"ci_low":0.7134565900802954,"coef":0.987593625326463,"p":2.9312739569229728e-12,"se":0.13969757878665584,"t":7.069511396720068,"term":"covariate_emotion_4"},{"ci_high":0.7658827755458804,"ci_low":0.1889511870151644,"coef":0.47741698128052235,"p":0.0012022763541400332,"se":0.14699937564238122,"t":3.2477483607956144,"term":"covariate_emotion_5"},{"ci_high":0.2058047752785886,"ci_low":0.1413690567258803,"coef":0.17358691600223444,"p":7.881067913677336e-25,"se":0.016417909132760252,"t":10.573022094260443,"term":"temporal_time"},{"ci_high":-0.22002343381270065,"ci_low":-0.45612151163599346,"coef":-0.33807247272434704,"p":2.483174336697894e-08,"se":0.060156647201061696,"t":-5.619868933094072,"term":"treatment_price_normalized"}],"first_stage":[{"ci_high":-0.586028401704251,"ci_low":-0.6718193758134914,"coef":-0.6289238887588712,"endog":"treatment_price_normalized","instrument":"instrument_z","p":8.018228202740839e-133,"se":0.02185912486076085,"t":-28.771686550354435}],"reduced_form":[{"ci_high":0.28076918148613617,"ci_low":0.14447452697011132,"coef":0.21262185422812374,"instrument":"instrument_z","p":1.3248017789767586e-09,"se":0.03472721812351072,"t":6.122628466003626}]},"tool":"infer.iv_2sls","version":"0.1.0","warnings":["controls dropped as collinear: ['covariate_emotion_6']"]},"result_for_model_ref":"aa83a4e7789aa5a4851c23e755584581127ba44e53ec5bd90ae6e38ba4ab4f42","result_sha256":"99e72804d20ff46d686c6903065d7fbd9e3d25934aebedf00df59dc784b756e4","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":2,"synthetic":false,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.977109Z","tool_id":"infer.iv_2sls","turn_id":"call_ae59e032"}
{"attempt":1,"attempts":[],"call_id":"call_58258b68","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"reference","model_requested":"reference","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["871733d7a01e2f911192b7cae3b95f08c3cc79ec7026220c7a4f8d34180ea98e","3e595670bb668979d9682d0c45915f4b048f8425c8a4f7cee164911448db5ecf","d9f3c49f11fb1f173465db87d9214b98e082b62564b2e23dc33e7b53801cf8fb"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a2be08c22418e5f99d74884496b58623f829b1cd34e8445acc19fc3a8299d1ac","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa (infer.iv_2sls): coef.treatment_price_normalized = -0,3381.","caveats":[],"figure_refs":["call_ba7ec5d1:coef.treatment_price_normalized"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"script_2"}]},"response_id":null,"run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":3,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.992412Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_9f4e6b05","epoch":1,"event":"guard","numbers_found":["-0,3381"],"retries_left":1,"run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":4,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.994363Z","turn_id":"call_58258b68","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa (infer.iv_2sls): coef.treatment_price_normalized = -0,3381.","answer_figure":{"call_id":"call_ba7ec5d1","ci_high":-0.22002343381270065,"ci_low":-0.45612151163599346,"formatted":-0.33807247272434704,"key":"coef.treatment_price_normalized","ref":"call_ba7ec5d1:coef.treatment_price_normalized","se":0.060156647201061696,"tool_id":"infer.iv_2sls","unit":"","value":-0.33807247272434704},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_ba7ec5d1:coef.treatment_price_normalized"],"run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":5,"stopped":"answered","task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.994528Z"}
{"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":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":6,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.994528Z"}
{"attempt":1,"check":{"check_index":1,"detail":"-0.3380724727 vs -0.338","expected":-0.338,"failure_class":null,"kind":"answer_figure_close","observed":-0.33807247272434704,"passed":true,"required":true,"source_id":"causcibench_real.gold.q153","tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":0.05,"rule":null},"trace_call_id":"call_ba7ec5d1","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":7,"task_id":"causcibench_real.q153","timestamp":"2026-10-15T17:11:03.994528Z"}