{"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/on-adaptive-propensity-score-truncation-in","title":"On Adaptive Propensity Score Truncation in Causal Inference","arxiv_id":"1707.05861","date":"2017-07-18","proceeding":null,"authors":["Cheng Ju","Joshua Schwab","Mark J. Van Der Laan"],"abstract":"The positivity assumption, or the experimental treatment assignment (ETA)\nassumption, is important for identifiability in causal inference. Even if the\npositivity assumption holds, practical violations of this assumption may\njeopardize the finite sample performance of the causal estimator. One of the\nconsequences of practical violations of the positivity assumption is extreme\nvalues in the estimated propensity score (PS). A common practice to address\nthis issue is truncating the PS estimate when constructing PS-based estimators.\nIn this study, we propose a novel adaptive truncation method,\nPositivity-C-TMLE, based on the collaborative targeted maximum likelihood\nestimation (C-TMLE) methodology. We demonstrate the outstanding performance of\nour novel approach in a variety of simulations by comparing it with other\ncommonly studied estimators. Results show that by adaptively truncating the\nestimated PS with a more targeted objective function, the Positivity-C-TMLE\nestimator achieves the best performance for both point estimation and\nconfidence interval coverage among all estimators considered.","url_abs":"http://arxiv.org/abs/1707.05861v1","url_pdf":"http://arxiv.org/pdf/1707.05861v1.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":"on-adaptive-propensity-score-truncation-in","repo_url":"https://github.com/jucheng1992/ctmle","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.05861","atlas_url":"https://app.syntology.ai/?focus=1707.05861","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}