Enabling Counterfactual Survival Analysis with Balanced Representations.

ABSTRACT: 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.e., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (e.g., 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 metri

SUBMITTER: Chapfuwa P 

PROVIDER: S-EPMC12423760 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature