{"id":"W4416133333","doi":"10.1111/2041-210x.70191","title":"Estimating causal effects with machine learning: A guide for ecologists","year":2025,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint John Regional Hospital; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Causal inference; Causal model; Leverage (statistics); Observational study; Instrumental variable; Artificial neural network; Deep learning; Confounding; Causal analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009728388,0.002397316,0.001913992,0.004615525,0.0006314703,0.004287823,0.004000785,0.003782723,0.01082628],"category_scores_gemma":[0.03185919,0.001947945,0.002082355,0.003290813,0.003526433,0.005807832,0.002459391,0.0114265,0.007148628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311302,"about_ca_system_score_gemma":0.002352203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002243662,"about_ca_topic_score_gemma":0.00269349,"domain_scores_codex":[0.9968922,0.001986075,0.0003287547,0.0002926638,0.0004501423,0.0000502879],"domain_scores_gemma":[0.9671091,0.02854109,0.0008076896,0.001384133,0.001575663,0.000582263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006778868,0.0003181594,0.002073034,0.002317322,0.0004119647,0.000361592,0.0005553635,0.03807093,0.001343879,0.2431167,0.3959222,0.315441],"study_design_scores_gemma":[0.00005897139,0.00003316821,0.0004555481,0.0008907026,0.00003304388,0.0001856696,0.0001278825,0.05009381,0.0004478939,0.6948039,0.2527913,0.0000781104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004324996,0.01815983,0.9481243,0.02300682,0.0007421235,0.0001839037,0.00132986,0.003091625,0.004928977],"genre_scores_gemma":[0.007584029,0.01621188,0.962531,0.004849732,0.001956138,0.001044169,0.0009620183,0.0008622851,0.003998723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01082628,"threshold_uncertainty_score":0.05144924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094575627814083,"score_gpt":0.3647507128542628,"score_spread":0.343804956576122,"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."}}