SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Appendix

This appendix is organized as follows: We first present an extended overview of the standard treatment effect estimation setup and discuss differences with the time-to-event setting (Appendix A). Appendix G contains the NeurIPS checklist. In the standard treatment effect estimation setup with binary or continuous outcomes (see e.g. No hidden confounders (1.a), Consistency (1.c) and Positivity/Overlap in treatment assignment (2.a) . Under the assumption of random censoring (which is discussed further in Appendix C.1), the ( t 1) Thus, the classification approach with log-loss is equivalent to optimizing for the likelihood of the hazard.