Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
This work addresses the challenge of stable and efficient causal inference estimation for researchers and practitioners in fields like healthcare and social sciences, representing an incremental improvement over existing methods.
The paper tackles the problem of estimating heterogeneous causal contrasts like conditional average treatment effects and conditional relative risks by introducing efficient plug-in (EP) learning, a framework that achieves oracle-efficiency while addressing drawbacks such as loss function non-convexity and instability from inverse probability weighting. In simulations, EP-learners outperform state-of-the-art competitors including T-learner, R-learner, and DR-learner.
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, including that (i) their practical applicability can be hindered by loss function non-convexity; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To avoid these drawbacks, EP-learner constructs an efficient plug-in estimator of the population risk function for the causal contrast, thereby inheriting the stability and robustness properties of plug-in estimation strategies like T-learning. 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 illustrate that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors, including T-learner, R-learner, and DR-learner. Open-source implementations of the proposed methods are available in our R package hte3.