Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
<|reference_start|>Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts: 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.<|reference_end|>
@article{van der laan2024combining,
    title={Combining T-learning and DR-learning: a framework for oracle-efficient
  estimation of causal contrasts},
    author={Lars van der Laan, Marco Carone, Alex Luedtke},
    journal={arXiv preprint arXiv:2402.01972},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01972},
    primaryClass={stat.ML cs.LG stat.ME}
}