高效调整复杂协变量：通过DOPE提升效率
Efficient adjustment for complex covariates: Gaining efficiency with DOPE
AI总结：
本文提出DOPE方法，通过调整协变量信息提升ATE估计效率，适用于高维和复杂数据。
摘要：
协变量调整是一种常用的用于从观测数据中估计平均治疗效应（ATE）的方法。假设数据生成模型已知的图结构，最近的结果给出了最优调整的图准则，这使得能够高效地估计ATE。然而，图方法在高维和复杂数据中具有挑战性，并且不容易指定非欧几里得数据（如文本）的有意义的图模型。我们提出了一种新的框架，可以调整任何由协变量表达的信息子集，并证明了最小足以预测结果给定治疗的信息也是最高效的调整信息。基于我们的理论结果，我们提出了去偏结果适应倾向估计器（DOPE）用于高效估计ATE，并在一般条件下提供了DOPE的渐近结果。与增强反向倾向加权（AIPW）估计器相比，DOPE可以在协变量高度预测治疗时仍保持其效率。我们通过单索引模型以及基于神经网络的DOPE实现，在模拟和真实数据上展示了其性能。我们的结果表明，DOPE为各种观测设置中的ATE估计提供了一种高效且稳健的方法。
英文摘要：
Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating model, recent results give graphical criteria for optimal adjustment, which enables efficient estimation of the ATE. However, graphical approaches are challenging for high-dimensional and complex data, and it is not straightforward to specify a meaningful graphical model of non-Euclidean data such as texts. We propose a new framework that accommodates adjustment for any subset of information expressed by the covariates, and we show that the information that is minimally sufficient for prediction of the outcome given the treatment is also most efficient for adjustment. Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estimator (DOPE) for efficient estimation of the ATE, and we provide asymptotic results for DOPE under general conditions. Compared to the augmented inverse propensity weighted (AIPW) estimator, DOPE can retain its efficiency even when the covariates are highly predictive of treatment. We illustrate this with a single-index model, and with an implementation of DOPE based on neural networks, we demonstrate its performance on simulated and real data. Our results show that DOPE provides an efficient and robust methodology for ATE estimation in various observational settings.
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