Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data

TL;DR

This paper introduces a flexible, orthogonal, and robust toolbox of survival learners for estimating heterogeneous treatment effects from censored time-to-event data, applicable in various study settings.

Contribution

The paper presents a novel, model-agnostic toolbox of orthogonal survival learners that incorporate custom weighting for improved robustness in low-overlap scenarios.

Findings

Learners are guaranteed to be orthogonal with favorable theoretical properties.

The toolbox includes neural survival learners, some of which are novel.

Empirical results show improved HTE estimation in low-overlap regimes.

Abstract

Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event data with censored outcomes (e.g., due to study dropout). In this paper, we propose a toolbox of novel orthogonal survival learners to estimate HTEs from time-to-event data under censoring. Our learners have three main advantages: (i) we show that learners from our toolbox are guaranteed to be orthogonal and thus come with favorable theoretical properties; (ii) our toolbox allows for incorporating a custom weighting function, which can lead to robustness against different types of low overlap, and (iii) our learners are model-agnostic (i.e., they can be combined with arbitrary machine learning models). We instantiate the learners from our toolbox using several weighting functions and, as a result, propose various neural orthogonal survival learners. Some of these coincide with existing survival learners (including survival versions of the DR- and R-learner), while others are novel and further robust w.r.t. low overlap regimes specific to the survival setting (i.e., survival overlap and censoring overlap). We then empirically verify the effectiveness of our learners for HTE estimation in different low-overlap regimes through numerical experiments. In sum, we provide practitioners with a large toolbox of learners that can be used for randomized and observational studies with censored time-to-event data.