Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing

TL;DR

This paper introduces a matching-based method to separately estimate how treatments affect both cure probability and event timing in time-to-event data with cured subpopulations, addressing limitations of standard survival analysis.

Contribution

It proposes a novel framework that constructs distinct match groups for estimating heterogeneous treatment effects on cure probability and event timing using mixture cure models and weighted matching.

Findings

Accurate estimation of treatment effects on cure probability and timing.

Robustness of the method demonstrated through simulations and real data.

Theoretical guarantees for estimator consistency.

Abstract

In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a cured subpopulation in which the event of interest never occurs. In such settings, treatment may have distinct effects on (1) the probability of being cured and (2) the event timing among non-cured individuals. Standard survival analysis and causal inference methods typically do not separate cured from non-cured individuals, obscuring distinct treatment mechanisms on cure probability and event timing. To address these challenges, we propose a matching-based framework that constructs distinct match groups to estimate heterogeneous treatment effects (HTE) on cure probability and event timing, respectively. We use mixture cure models to identify feature importance for both estimands, which in turn informs weighted distance metrics for matching in high-dimensional spaces. Within matched groups, Kaplan-Meier estimators provide estimates of cure probability and expected time to event, from which individual-level treatment effects are derived. We provide theoretical guarantees for estimator consistency and distance metric optimality under an equal-scale constraint. We further decompose estimation error into contributions from censoring, model fitting, and irreducible noise. Simulations and real-world data analyses demonstrate that our approach delivers interpretable and robust HTE estimates in time-to-event settings.