ICML 2025

Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing
Censored Data with Recursively Imputed Trees
Tomer Meir 1 Uri Shalit 1 2 Malka Gorfine 2
Abstract
Tailoring treatments to individual needs is a cen-
tral goal in fields such as medicine. A key step to-
ward this goal is estimating Heterogeneous Treat-
ment Effects (HTE)—the way treatments impact
different subgroups. While crucial, HTE estima-
tion is challenging with survival data, where time
until an event (e.g., death) is key. Existing meth-
ods often assume complete observation, an as-
sumption violated in survival data due to right-
censoring, leading to bias and inefficiency. Cui
et al. (2023) proposed a doubly-robust method
for HTE estimation in survival data under no hid-
den confounders, combining a causal survival for-
est with an augmented inverse-censoring weight-
ing estimator. However, we find it struggles un-
der heavy censoring, which is common in rare-
outcome problems such as Amyotrophic lateral
sclerosis (ALS). Moreover, most current meth-
ods cannot handle instrumental variables, which
are a crucial tool in the causal inference arsenal.
We introduce Multiple Imputation for Survival
Treatment Response (MISTR), a novel, general,
and non-parametric method for estimating HTE
in survival data. MISTR uses recursively imputed
survival trees to handle censoring without directly
modeling the censoring mechanism. Through ex-
tensive simulations and analysis of two real-world
datasets—the AIDS Clinical Trials Group Proto-
col 175 and the Illinois unemployment dataset we
show that MISTR outperforms prior methods un-
der heavy censoring in the no-hidden-confounders
setting, and extends to the instrumental variable
setting. To our knowledge, MISTR is the first non-
parametric approach for HTE estimation with un-
observed confounders via instrumental variables.
1Department of Data and Decisions Sciences, Technion, Haifa,
Israel 2Department of Statistics and Operations Research, Tel Aviv
Univers...