# Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data

arXiv: https://arxiv.org/abs/2412.19711 · HTML: https://mlsanity.com/paper/2412.19711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Orthogonal Statistical Learning](https://mlsanity.com/paper/1901.09036/) (2019)
- [Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts](https://mlsanity.com/paper/2402.01972/) (2024)
- [Sequential Double Robustness in Right-Censored Longitudinal Models](https://mlsanity.com/paper/1705.02459/) (2017)
- [To Impute or not to Impute? Missing Data in Treatment Effect Estimation](https://mlsanity.com/paper/2202.02096/) (2022)
- [Orthogonal prediction of counterfactual outcomes](https://mlsanity.com/paper/2311.09423/) (2023)
- [Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/2007.02852/) (2020)
- [Debiased inference for a covariate-adjusted regression function](https://mlsanity.com/paper/2210.06448/) (2022)
- [Selecting optimal subgroups for treatment using many covariates](https://mlsanity.com/paper/1802.09642/) (2018)
- [Regression in Tensor Product Spaces by the Method of Sieves](https://mlsanity.com/paper/2206.02994/) (2022)
- [Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients](https://mlsanity.com/paper/2311.04359/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning Who to Treat When Treatment is Missing](https://mlsanity.com/paper/2607.14346/) (2026)

## Abstract (arXiv)

When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represented. In this work, we discuss this commonly overlooked problem and consider the impact that missing at random (MAR) outcome data has on causal machine learning estimators for the conditional average treatment effect (CATE). We propose two de-biased machine learning estimators for the CATE, the mDR-learner and mEP-learner, which address the issue of under-representation by integrating inverse probability of censoring weights into the DR-learner and EP-learner respectively. We show that under reasonable conditions, these estimators are oracle efficient, and illustrate their favorable performance through simulated data settings, comparing them to existing CATE estimators, including comparison to estimators which use common missing data techniques. We present an example of their application using the GBSG2 trial, exploring treatment effect heterogeneity when comparing hormonal therapies to non-hormonal therapies among breast cancer patients post surgery, and offer guidance on the decisions a practitioner must make when implementing these estimators.