{"id":"W4416525192","doi":"10.1016/j.jdeveco.2025.103655","title":"Causal Inference with Predicted Outcomes: Correcting prediction error bias in satellite-based impact evaluation","year":2025,"lang":"en","type":"article","venue":"Journal of Development Economics","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Consortium of International Agricultural Research Centers","keywords":"Causal inference; Unobservable; Inference; Measure (data warehouse); Causal model; Mean squared prediction error; Observational error; Yield (engineering); Agriculture","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001022037,0.0001691605,0.0002960709,0.00009822982,0.00007950119,0.00009803686,0.0001630566,0.0001047089,0.000110619],"category_scores_gemma":[0.0003313157,0.00005991227,0.00006543349,0.0003794102,0.00001834037,0.0002895605,0.00002400522,0.0002109325,0.000002271242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008224465,"about_ca_system_score_gemma":0.0003647432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004409498,"about_ca_topic_score_gemma":0.002512523,"domain_scores_codex":[0.9987029,0.00008065621,0.000660151,0.000156829,0.0001796504,0.0002198046],"domain_scores_gemma":[0.9986944,0.0003536644,0.0005442444,0.00003788895,0.0002787556,0.00009104147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001472637,0.0001116914,0.9442457,0.000008038335,0.0000580952,0.000002659055,0.0002599738,0.00488979,0.0008787233,0.000002590867,0.00006450716,0.04933099],"study_design_scores_gemma":[0.0007671952,0.0002376385,0.9948763,0.0002041624,0.00002642782,0.00001327099,0.0006658459,0.001607361,0.001006228,0.00004000438,0.0004373764,0.0001182499],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984003,0.0000536766,0.00002410403,0.0007352274,0.000292518,0.0002758604,0.00002493683,0.00001629274,0.0001771399],"genre_scores_gemma":[0.9992865,0.00004433196,0.0003370802,0.0001529573,0.00005345346,0.00000843026,0.00008617574,0.000001110946,0.0000299074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05063056,"threshold_uncertainty_score":0.2443151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0878181238155162,"score_gpt":0.314617576754914,"score_spread":0.2267994529393978,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}