Enabling Counterfactual Survival Analysis with Balanced Representations
This work addresses a gap in counterfactual survival analysis for medical and industrial domains, offering a novel method that improves accuracy in treatment-effect estimation.
The paper tackles the problem of counterfactual inference for survival outcomes, which are common in medical and industrial applications, by proposing a unified framework that accounts for censored events and introduces a nonparametric hazard ratio metric. The approach significantly outperforms competitive alternatives in survival-outcome prediction and treatment-effect estimation on real-world and semi-synthetic datasets.
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 metric for evaluating average and individualized treatment effects. Experimental results on real-world and semi-synthetic datasets, the latter of which we introduce, demonstrate that the proposed approach significantly outperforms competitive alternatives in both survival-outcome prediction and treatment-effect estimation.