{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/consistent-estimation-of-propensity-score","title":"Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects","arxiv_id":"1805.07684","date":"2018-05-20","proceeding":null,"authors":["Sherri Rose"],"abstract":"Observational cohort studies with oversampled exposed subjects are typically\nimplemented to understand the causal effect of a rare exposure. Because the\ndistribution of exposed subjects in the sample differs from the source\npopulation, estimation of a propensity score function (i.e., probability of\nexposure given baseline covariates) targets a nonparametrically nonidentifiable\nparameter. Consistent estimation of propensity score functions is an important\ncomponent of various causal inference estimators, including double robust\nmachine learning and inverse probability weighted estimators. This paper\ndevelops the use of the probability of exposure from the source population in a\nflexible computational implementation that can be used with any algorithm that\nallows observation weighting to produce consistent estimators of propensity\nscore functions. Simulation studies and a hypothetical health policy\nintervention data analysis demonstrate low empirical bias and variance for\nthese propensity score function estimators with observation weights in finite\nsamples.","url_abs":"http://arxiv.org/abs/1805.07684v2","url_pdf":"http://arxiv.org/pdf/1805.07684v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"consistent-estimation-of-propensity-score","repo_url":"https://github.com/sherrirose/ConditionalCohortSamples","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[{"method_slug":"causal-inference","method_name":"Causal inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}