Estimating heterogeneous treatment effects with right-censored data via causal survival forests

Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2023 · 90 citations

• Computer Science
• Machine Learning
• Statistics

Abstract Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.

policytree: Policy learning via doubly robust empirical welfare maximization over trees

The Journal of Open Source Software · 2020 · 46 citations

• Computer Science
• Artificial Intelligence
• Machine Learning

The problem of learning treatment assignment policies from randomized or observational data arises in many fields. For example, in personalized medicine, we seek to map patient observables (like age, gender, heart pressure, etc.) to a treatment choice using a data-driven rule.