{"attempt":1,"datasets":{"cai_effect_microinsurance":{"description_shown":"The dataset is the result of a randomized controlled trial conducted to study the effect of insurance on farming behavior. Each village consists of at least one AHW (Animal Husbandry Worker), who is involved in tasks such as animal immunization, providing technical assistance to farmers, etc. As part of the experiment, AHWs are assigned to different incentive schemes associated with the number of sow insurance purchases. In the control group villages, AHWs were offered a reward of 50 yuan to participate. In the low incentive group (LIG), the AHW is offered 20 Yuan to begin with, and an additional payment of 2 Yuan for each insured sow. Finally, in the high incentive group (HIG), the AHWs are offered a starting payment of 20 Yuan and an additional 4 Yuan for each insured sow. At the end of the experiment, data on total sows in each village were obtained.\n\nVariables:\nsow0: No. of Sows in Dec. 2006\nsow1: No. of Sows in Sept. 2007\nsow2: No. of Sows in Dec. 2007\npig: No. of Pigs in Dec. 2006\ngoat: No. of Goats in Dec 2006\npop_regist: Village Population\npop_migout: No. of Villagers as Migrant Workers\nage_vil: Ave. Villager Age\nedu_vil: Ave. Villager Education (Years)\nmale_vil: Fraction Male in Village\nland: Land per Household (Mu)\nhouse_val: House Value (RMB yuan)\nlhouse_val: Log House Value\nsurname_vil: No. of Surnames in the Village\nno_xnh: No. of Villagers in New Medical Coop. Scheme\nsubsidy: No. of Households Receiving Gov. Subsidy\ninsuredpig: No. of Insured Sows\nsnow: No. of Sow Deaths in Snow Storm\nsow3: No. of Sows in March 2008\nsow4: No. of Sows in June 2008\ngroup: Coding for different groups (1 = Control Group; 2 = Low incentive Group; 3 = High Incentive Group)\ngroupd1: Control Group Dummy\ngroupd2: Low Incentive Group Dummy\ngroupd3: High Incentive Dummy\nvillageid: Village Id\nvillagename: Village Name (in Chinese)\nsnow_insured: No. of Sow Deaths in Snow Storm Interact with No. of Insured Sows\ntowndummy1-26: 26 Town dummies\nsow2008: No. of sows at the end of 2008\nsow2009: No. of sows at the end of 2009\nsow2010: No. of sows at the end of 2010\nsow2011: No. of sows at the end of 2011\npig2008: No. of pigs at the end of 2008\npig2009: No. of pigs at the end of 2009\npig2010: No. of pigs at the end of 2010\npig2011: No. of pigs at the end of 2011\nsheep2008: No. of sheep at the end of 2008\ncow2008: No. of cows at the end of 2008\nsow200810: Average sows 2008-2010\npig200810: Average pigs 2008-2010\nlnhouse: Log House Value\ninsured_house: Insured Sows _ Log House Value\ninsured_edu: Insured Sows _ Avg. Villager Education\nsnow_house: Sow Deaths _ Log House Value\nsnow_edu: Sow Deaths _ Avg. Villager Education\ninsured_house_snow: insured_house _ snow\ninsured_edu_snow: insured_edu _ snow\nhouse_groupd2: Log House Value _ Low Incentive Dummy\nhouse_groupd3: Log House Value _ High Incentive Dummy\nhouse_snow_gd2: snow_house _ Low Incentive Dummy\nhouse_snow_gd3: snow_house _ High Incentive Dummy\nedu_groupd2: Avg. Villager Education _ Low Incentive Dummy\nedu_groupd3: Avg. Villager Education _ High Incentive Dummy\nedu_snow_gd2: insured_edu _ snow _ Low Incentive Dummy\nedu_snow_gd3: insured_edu _ snow _ High Incentive Dummy","file_sha256":"e3249019db8a6d5cd72c88756b60b22681a35b469f71e420f6e49bfa747360da","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"What is the causal effect of having a sow insured on the subsequent number of sows owned by a farmer?","question_sha256":"0cff41ef4a5fd2f2fb1f900a5ca90a484cb0ad555a7e298818c7d8433437ef6c","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d","seq":0,"skills":[],"task_id":"causcibench_real.q125","task_sha256":"040aec903fa70074cf2663ed7f1a7f8df45e0202e9d75a04dd6078c148732e3b","timestamp":"2026-10-15T17:11:02.572327Z"}
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{"arguments":{"cluster":null,"dataset":"cai_effect_microinsurance","endog":["insuredpig"],"exog":[],"instruments":["groupd2","groupd3"],"level":0.95,"se":"HC1","subset":null,"y":"sow3"},"attempt":1,"cached":false,"call_id":"call_c7aee20c","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"cai_effect_microinsurance":"cdd6961e1779d8abe1e6f2ed9404f07595871ef43fe70583c3308e396368b57a"},"decided_by":"model","duration_ms":4.333249991759658,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"8108683a2e317233e869435f3398f00a39e8e7e7750cc9409c59e982a360ab2c","llm_name":"infer__iv_2sls","output_digest":"9aa74de315d4f5ab4f896a80f63ea826e827c6bd518571edbfe2f2f53a88506e","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_c7aee20c","datasets":{},"diagnostics":[{"flag":false,"name":"first_stage_F.insuredpig","note":"Wald F of the excluded instruments in the first stage for insuredpig (heteroskedasticity-robust standard errors (HC1))","value":14.788955277415837},{"flag":false,"name":"partial_r2.insuredpig","note":"share of the variation the instruments explain beyond the controls","value":0.04184280964985643},{"flag":false,"name":"weak_instrument","note":"every first-stage F is at least 10","value":false},{"flag":false,"name":"overid_p","note":"Sargan test of the overidentifying restrictions (assumes homoskedastic errors)","value":0.22256487265624544},{"flag":false,"name":"sargan","note":"n\u00b7R\u00b2 of the structural residuals on the instruments","value":1.4877565346306791},{"flag":false,"name":"sargan_df","note":"","value":1},{"flag":false,"name":"n_obs","note":"","value":321},{"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":1.3788580503281227,"ci_level":0.95,"ci_low":0.4296830951116095,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.9042705727198661,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":321.0},{"note":"","symbol":"instrumentos","unit":"","value":2.0}],"text_pt":"Coeficiente de insuredpig por m\u00ednimos quadrados em dois est\u00e1gios: sow3 sobre as proje\u00e7\u00f5es dos regressores end\u00f3genos nos instrumentos ['groupd2', 'groupd3']; 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