Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

TL;DR

This paper introduces EP-learning, a new framework for estimating heterogeneous causal effects that combines the stability of plug-in methods with the efficiency of Neyman-orthogonal strategies, outperforming existing methods.

Contribution

The paper proposes EP-learning, a novel plug-in based framework that achieves oracle efficiency for causal contrast estimation, addressing limitations of existing methods like DR-learning and T-learning.

Findings

EP-learners are oracle-efficient and asymptotically equivalent to debiased estimators.

EP-learners outperform T-, R-, and DR-learners in simulation experiments.

Open-source R package exttt{hte3} implements the proposed methods.

Abstract

We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks: (i) their practical applicability can be hindered by non-convex loss functions; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To overcome these issues, the EP-learner leverages an efficient plug-in estimator of the population risk function for the causal contrast. In doing so, it inherits the stability of plug-in strategies such as T-learning, while improving on their efficiency. Under reasonable conditions, EP-learners based on empirical risk minimization are oracle-efficient, exhibiting asymptotic equivalence to the minimizer of an oracle-efficient one-step debiased estimator of the population risk function. In simulation experiments, we show that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors, including the T-learner, R-learner, and DR-learner. Open-source implementations of the proposed methods are available in our \texttt{R} package \texttt{hte3}.