{"doi":"10.1093/biomet/asae034","title":"Semiparametric efficiency gains from parametric restrictions on propensity scores","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.</jats:p>","journal":"Biometrika","year":2025,"id":616863,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":1,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1590550,"name":"Haruki Kono","orcid":"0000-0001-9253-9764","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Semiparametric efficiency gains from parametric restrictions on propensity scores","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":1,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4400387138","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.1008879,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":"https://academic.oup.com/pages/standard-publication-reuse-rights","oa_locations":[{"url":"https://academic.oup.com/biomet/advance-article-pdf/doi/10.1093/biomet/asae034/58869225/asae034.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/biomet/article-pdf/112/1/asae034/58869225/asae034.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/biomet/asae034","host_type":"journal"},{"url":"http://hdl.handle.net/10.1093/biomet/asae034","host_type":"repository"}],"fields_of_study":["Advanced Causal Inference Techniques","Statistical Methods and Inference","Statistical Methods and Bayesian Inference","Economics","Mathematics"],"mesh_terms":[],"keywords":["Parametric statistics","Covariate","Semiparametric model","Mathematics","Propensity score matching","Econometrics","Space (punctuation)","Parametric model","Partition (number theory)","Parameter space","Efficiency","Outcome (game theory)","Mathematical optimization","Statistics","Mathematical economics","Computer science","Combinatorics","Estimator"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Affordable and clean energy"}],"linked_datasets":[{"doi":"10.48550/arxiv.2306.04177","title":"Semiparametric Efficiency Gains From Parametric Restrictions on Propensity Scores","publisher":"arXiv","resource_type":"Text"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T00:08:25.517866Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}