{"id":"W4388095903","doi":"10.1007/978-3-031-35763-3_3","title":"Optimal Transport for Counterfactual Estimation: A Method for Causal Inference","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in systems, decision and control","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Covariate; Counterfactual thinking; Causal inference; Dimension (graph theory); Population; Econometrics; Mathematics; Average treatment effect; Quantile; Inference; Variable (mathematics); Instrumental variable; Statistics; Propensity score matching; Computer science; Psychology; Artificial intelligence; Combinatorics; Medicine; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01071928,0.001916272,0.003415784,0.002906796,0.00147578,0.003594356,0.003840504,0.003161821,0.01248618],"category_scores_gemma":[0.04198653,0.002389478,0.004162625,0.00420534,0.003876684,0.005964023,0.003576866,0.006271664,0.00208587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002322676,"about_ca_system_score_gemma":0.003128384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005634867,"about_ca_topic_score_gemma":0.003484495,"domain_scores_codex":[0.9939393,0.004371325,0.0003015434,0.0006190781,0.000617892,0.0001508365],"domain_scores_gemma":[0.9808738,0.01625372,0.0005025278,0.001488234,0.0007449138,0.00013676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009914822,0.00005208486,0.000305083,0.0003201814,0.0002570939,0.0001045513,0.0001977309,0.07235854,0.0004902931,0.8517006,0.004682193,0.06943246],"study_design_scores_gemma":[0.000024989,0.00002529854,0.0001068814,0.00006283643,0.00008806168,0.00006033482,0.0000274562,0.3026305,0.0003924366,0.6897575,0.00679142,0.00003225664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002049151,0.0002060912,0.9986966,0.00009964312,0.00004552021,0.00001641804,0.0000357702,0.00008122481,0.0006139312],"genre_scores_gemma":[0.04089535,0.001559752,0.9483243,0.0002208586,0.0003546084,0.0005059921,0.0003243257,0.0005492168,0.007265567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01248618,"threshold_uncertainty_score":0.05668968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2867639985625557,"score_gpt":0.5008328927146684,"score_spread":0.2140688941521126,"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."}}