title: 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 (for equipment
  monitoring). When the outcome of interest is 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 which we introduce, demonstrate that the proposed approach significantly
  outperforms competitive alternatives in both survival-outcome predictions and
  treatment-effect estimation.
authors: Paidamoyo Chapfuwa Serge Assaad Shuxi Zeng Michael Pencina Lawrence Carin Ricardo Henao
hIndex: Michael Pencina 102