{"id":"W7117117856","doi":"10.54254/2753-8818/2026.ch30759","title":"Causal Inference with Observational Data via Propensity Score Matching: A Simple, Hands‑On Guide","year":2025,"lang":"","type":"article","venue":"Theoretical and Natural Science","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Propensity score matching; Causal inference; Observational study; Matching (statistics); Checklist; Replication (statistics); Workflow; Baseline (sea); Simple (philosophy)","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002343992,0.0005497129,0.0006400084,0.0002177645,0.0009525096,0.0004521224,0.002198175,0.0001870007,0.0001776426],"category_scores_gemma":[0.004104596,0.0003538269,0.00003960561,0.001654921,0.01000838,0.001371366,0.00266774,0.001079144,0.0000145211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001760788,"about_ca_system_score_gemma":0.001069112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006252615,"about_ca_topic_score_gemma":0.00004843659,"domain_scores_codex":[0.9955381,0.0001584304,0.0006797547,0.001463995,0.001227216,0.0009325425],"domain_scores_gemma":[0.9952288,0.001956442,0.0002400154,0.001563086,0.0006812245,0.000330433],"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":[0.0003583003,0.0001418873,0.002088341,0.000128122,0.00002145927,0.00002078197,0.00012015,0.000006022201,0.004822222,0.97865,0.0002401999,0.01340252],"study_design_scores_gemma":[0.0005123035,0.0007057298,0.01447979,0.0009350343,0.00009067697,0.00003325635,0.00004094979,0.01359249,0.01085814,0.9580238,0.0001839506,0.0005438074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8478291,0.0001579803,0.1399516,0.003044336,0.0002056544,0.001234705,0.0000837576,0.0002727875,0.007220031],"genre_scores_gemma":[0.9793907,0.00009376049,0.01874647,0.001239297,0.00006103077,0.00001931955,0.00003638043,0.00001890222,0.0003941657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1315616,"threshold_uncertainty_score":0.9998914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1522939937492116,"score_gpt":0.4137528535260652,"score_spread":0.2614588597768536,"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."}}