biostudies-literatureChapfuwa PNIBIB NIH HHSNINDS NIH HHS133-145https://www.ebi.ac.uk/biostudies/studies/S-EPMC12423760biostudies-literatureUnknown2021Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications, <i>i.e</i>., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (<i>e.g</i>., for equipment monitoring). When the outcome of interest is a time-to-event, special precautions for handling censored events need to be taken, as ignoring censored outcomes may lead to biased estimates. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes. Further, we formulate a nonparametric hazard ratio metriACM CHIL 2021 : proceedings of the 2021 ACM Conference on Health, Inference, and Learning : April 8-9, 2021, Virtual Event. ACM Conference on Health, Inference, and Learning (2021 : Online)Enabling Counterfactual Survival Analysis with Balanced Representations.PMC12423760R01 EB025020R61 NS120246Zeng SHenao RChapfuwa PCarin LAssaad SPencina MJfalseEnabling Counterfactual Survival Analysis with Balanced Representations.Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications, <i>i.e</i>., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (<i>e.g</i>., for equipment monitoring). When the outcome of interest is a time-to-event, special precautions for handling censored events need to be taken, as ignoring censored outcomes may lead to biased estimates. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes. Further, we formulate a nonparametric hazard ratio metri2021-01-01T00:00:00Z2021 Apr2026-06-03T07:43:39.56Z2026-04-26T03:09:40.504ZS-EPMC124237604094856610.1145/3450439.3451875