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

• URL: http://arxiv.org/abs/2402.01972v1
• Date: Sat, 3 Feb 2024 00:47:50 GMT
• Title: Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
• Authors: Lars van der Laan, Marco Carone, Alex Luedtke
• Abstract summary: We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts. EP-learners of the conditional average treatment and conditional relative risk outperform state-of-the-art competitors.
• Score: 1.0896141997814233
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

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