{"id":"W3123549106","doi":"10.1097/ede.0000000000001315","title":"Incremental Propensity Score Effects for Time-fixed Exposures","year":2021,"lang":"en","type":"article","venue":"Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; University of California, Irvine; Northwestern University; University of Pittsburgh; University of Pennsylvania; National Institutes of Health; RTI International; Case Western Reserve University; National Science Foundation","keywords":"Propensity score matching; Causal inference; Medicine; Preeclampsia; Average treatment effect; Statistics; Odds ratio; Consistency (knowledge bases); Psychological intervention; Econometrics; Pregnancy; Demography; Mathematics; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001497466,0.0001782879,0.0006893481,0.00003648232,0.00009031044,0.00000488468,0.0001538225,0.0001814638,0.0001295596],"category_scores_gemma":[0.02504639,0.0001521069,0.0001207308,0.00007736321,0.000113257,0.00008074859,0.0001608564,0.0001759177,0.00004612522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007098536,"about_ca_system_score_gemma":0.00005679544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009434072,"about_ca_topic_score_gemma":0.00002229483,"domain_scores_codex":[0.9980741,0.0006467022,0.0004454499,0.0003717553,0.00006282803,0.0003991351],"domain_scores_gemma":[0.9906256,0.008549745,0.0001987851,0.0004085481,0.0001407085,0.00007664596],"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":[0.0001548248,0.0003000477,0.04509815,0.0006253666,0.0001793638,0.00008603344,0.0001695759,0.000006135542,0.2254333,0.5773771,0.1384866,0.01208352],"study_design_scores_gemma":[0.000346708,0.0002739059,0.004255081,0.00007365928,0.00003439579,0.00004038866,0.000009267466,0.0001057658,0.1993754,0.7934387,0.001850156,0.0001965295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6864281,0.0003830703,0.3071939,0.001486198,0.0002740105,0.00137329,0.00002128822,0.0006377379,0.002202466],"genre_scores_gemma":[0.416298,0.00003164551,0.5783775,0.002841963,0.0002952404,0.0004860149,0.00007553243,0.00005994926,0.001534135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2711836,"threshold_uncertainty_score":0.983166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3565244023728392,"score_gpt":0.4500213977903498,"score_spread":0.09349699541751066,"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."}}