Integrative R-learner of heterogeneous treatment effects combining experimental and observational studies
Integrative R-learner of heterogeneous treatment effects combining experimental and observational studies
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发表时间：
2022
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通讯作者：
Lili Wu;Shu Yang
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作者：
Lili Wu;Shu Yang
估计异质性治疗效应（HTE）的金标准方法是随机对照试验（RCT）或对照实验研究，其中治疗随机化减轻了混淆偏倚。然而，实验数据通常是小样本量和有限的主题的多样性，由于昂贵的成本。另一方面，大型观察性研究（OS）变得越来越受欢迎和可访问。然而，操作系统可能会受到隐藏的混淆，其存在是不可测试的。我们通过利用实验数据进行识别和观察数据来提高效率，为HTE和混淆函数开发了一个综合R学习器。我们形成了一个正则化的损失函数的HTE和混杂函数，承担奈曼正交属性，允许不灵活的模型的滋扰函数估计。所提出的综合R -学习器的关键新奇在于为HTE和混杂函数施加不同的正则化项，使得混杂函数的可能的平滑性或稀疏性可以被利用来改善HTE估计。我们的综合R学习器有两个贝内：首先，它提供了一个通用框架，可以容纳各种HTE模型以实现损失最小化;其次，在没有OS中隐藏混杂的任何先验知识的情况下，所提出的综合R学习器是一致的，并且渐进地至少与仅使用RCT的估计器一样有效。基于广泛的模拟和实际数据的应用程序，从教育实验的基础上的实验表明，所提出的综合R -学习者优于替代方法。
The gold-standard approach to estimating heterogeneous treatment effects (HTEs) is randomized controlled trials (RCTs) or controlled experimental studies, where treatment randomization mitigates confounding biases. However, experimental data are usually small in sample size and limited in subjects’ diversity due to expensive costs. On the other hand, large observational studies (OSs) are becoming increasingly popular and accessible. However, OSs might be subject to hidden confounding whose existence is not testable. We develop an integrative R -learner for the HTE and confounding function by leveraging experimental data for identification and observational data for boosting efficiency. We form a regularized loss function for the HTE and confounding function that bears the Neyman orthogonality property, allowing flexible models for the nuisance function estimation. The key novelty of the proposed integrative R -learner is to impose different regularization terms for the HTE and confounding function so that the possible smoothness or sparsity of the confounding function can be leveraged to improve HTE estimation. Our integrative R -learner has two benefits: first, it provides a general framework that can accommodate various HTE models for loss minimization; second, without any prior knowledge of hidden confounding in the OS, the proposed integrative R -learner is consistent and asymptotically at least as efficient as the estimator using only the RCT. The experiments based on extensive simulation and a real-data application adapted from an educational experiment show that the proposed integrative R -learner outperforms alternative approaches.