TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis

TL;DR

TV-SurvCaus introduces a novel neural framework for causal survival analysis with time-varying treatments, providing theoretical guarantees and outperforming existing methods on synthetic and real data.

Contribution

It extends representation balancing techniques to dynamic treatment regimes with survival outcomes, offering theoretical bounds and a sequence modeling neural architecture.

Findings

Outperforms existing methods in estimating treatment effects

Provides theoretical guarantees for dynamic treatment regimes

Demonstrates effectiveness on synthetic and real-world datasets

Abstract

Estimating the causal effect of time-varying treatments on survival outcomes is a challenging task in many domains, particularly in medicine where treatment protocols adapt over time. While recent advances in representation learning have improved causal inference for static treatments, extending these methods to dynamic treatment regimes with survival outcomes remains under-explored. In this paper, we introduce TV-SurvCaus, a novel framework that extends representation balancing techniques to the time-varying treatment setting for survival analysis. We provide theoretical guarantees through (1) a generalized bound for time-varying precision in estimation of heterogeneous effects, (2) variance control via sequential balancing weights, (3) consistency results for dynamic treatment regimes, (4) convergence rates for representation learning with temporal dependencies, and (5) a formal bound on the bias due to treatment-confounder feedback. Our neural architecture incorporates sequence modeling to handle temporal dependencies while balancing time-dependent representations. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments. Our framework advances the field of causal inference by enabling more accurate estimation of treatment effects in dynamic, longitudinal settings with survival outcomes.