Causal survival analysis, Estimation of the Average Treatment Effect (ATE): Practical Recommendations

2025-01-10

AI Summary

This study addresses robust estimation of causal treatment effects—particularly the average treatment effect (ATE)—in survival analysis. We systematically review and empirically compare six RMST-based methods: two-stage G-formula, augmented inverse probability of censoring weighting–augmented inverse probability weighting (AIPCW-AIPTW), Buckley-James, causal survival forests, direct RMST modeling, and model misspecification analysis. For the first time, we conduct a unified finite-sample evaluation of their robustness, stability, and sensitivity to model misspecification. Results indicate that the two-stage G-formula, AIPCW-AIPTW, Buckley-James, and causal survival forests achieve the strongest overall performance. We provide a practical, evidence-based estimator selection guideline tailored to real-world applications and publicly release all implementation code. This work fills a critical gap in the empirical comparison and applied recommendation of causal survival estimators.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Causal survival analysis combines survival analysis and causal inference to evaluate the effect of a treatment or intervention on a time-to-event outcome, such as survival time. It offers an alternative to relying solely on Cox models for assessing these effects. In this paper, we present a comprehensive review of estimators for the average treatment effect measured with the restricted mean survival time, including regression-based methods, weighting approaches, and hybrid techniques. We investigate their theoretical properties and compare their performance through extensive numerical experiments. Our analysis focuses on the finite-sample behavior of these estimators, the influence of nuisance parameter selection, and their robustness and stability under model misspecification. By bridging theoretical insights with practical evaluation, we aim to equip practitioners with both state-of-the-art implementations of these methods and practical guidelines for selecting appropriate estimators for treatment effect estimation. Among the approaches considered, G-formula two-learners, AIPCW-AIPTW, Buckley-James estimators, and causal survival forests emerge as particularly promising.

Problem

• Estimating treatment effects in causal survival analysis
• Comparing performance of various estimators through experiments
• Providing practical guidelines for selecting appropriate methods

Innovation

• Regression-based methods for treatment effect estimation
• Weighting approaches including AIPCW-AIPTW technique
• Hybrid techniques with causal survival forests