Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there are important theoretical gaps in understanding if and when such methods are optimal. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Our work contributes in several main ways. First, we study a two-stage doubly robust CATE estimator and give a generic model-free error bound, which, despite its generality, yields sharper results than those in the current literature. We apply the bound to derive error rates in nonparametric models with smoothness or sparsity, and give sufficient conditions for oracle efficiency. Underlying our error bound is a general oracle inequality for regression with estimated or imputed outcomes, which is of independent interest; this is the second main contribution. The third contribution is aimed at understanding the fundamental statistical limits of CATE estimation. To that end, we propose and study a local polynomial adaptation of double-residual regression. We show that this estimator can be oracle efficient under even weaker conditions, if used with a specialized form of sample splitting and careful choices of tuning parameters. These are the weakest conditions currently found in the literature, and we conjecture that they are minimal in a minimax sense. We go on to give error bounds in the non-trivial regime where oracle rates cannot be achieved. Some finite-sample properties are explored with simulations.
Causal inference through multi-stage learning and doubly robust deep neural networks
Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）；Halicioglu Data Science Institute, University of California, San Diego（加利福尼亚大学圣地亚哥分校哈利乔卢数据科学研究院）
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts
We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function . We focus on a class of parameters that have influence function which depends on two infinite dimensional nuisance functions and such that the bias of the one-step estimator of the parameter of interest is the expectation of the product of the estimation errors of the two nuisance functions. Our class includes many important treatment effect contrasts of interest in causal inferen…
Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
未测量的混杂和选择偏倚在观察性研究中经常引起关注，如果不适当解释，可能会使因果分析无效。在结果依赖性抽样下，对治疗、结果和样本选择过程具有因果影响的潜在因素可能导致不可测量的混杂和选择偏倚，从而在没有额外假设的情况下无法识别标准因果参数。在处理效应的比值比模型下，Li et al. 2022通过利用一对阴性对照变量作为混杂和选择偏倚来源的潜在因素的代理，建立了因果效应的近端识别和估计。然而，他们的方法完全依赖于所谓的治疗混淆桥函数的存在和正确规范，这是一种限制治疗分配机制的模型。在这篇文章中，我们提出了双重稳健估计的优势比模型下的两个滋扰功能-治疗混杂桥函数和结果混杂桥函数，限制了结果的法律，使我们的估计是一致的，渐近正常的，如果桥函数模型是正确的指定，而不知道哪一个是。因此，我们提出的双稳健估计量可能比Li等人的估计量更稳健。我们的模拟证实，提出的优势比因果效应的近端估计可以充分考虑剩余混杂和选择偏差，在规定的条件下，校准良好的置信区间在广泛的情况下，标准方法通常无法保持一致。此外，建议的双重稳健估计是一致的，如果至少有一个混杂桥函数被正确指定。
Doubly Robust Proximal Causal Learning for Continuous Treatments
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）；Zhangjiang Fudan International Innovation Center（张江复旦国际创新中心）；Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University)（复旦大学计算神经科学与类脑智能重点实验室）；MOE Frontiers Center for Brain Science, Fudan University（教育部脑科学前沿中心（复旦大学））；Shanghai Engineering Research Center of AI & Robotics, Fudan University（上海市人工智能与机器人工程技术研究中心（复旦大学））；Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University（教育部人工智能与机器人工程研究中心（复旦大学））
Auto-Doubly Robust Estimation of Causal Effects on a Network
Peking University, HSBC Business School（北京大学汇丰商学院）；Shanghai Jiao Tong University, Antai College of Economics and Management（上海交通大学安泰经济与管理学院）；University of Pennsylvania, The Wharton School（宾夕法尼亚大学沃顿商学院）
Why Is the Double-Robust Estimator for Causal Inference Not Doubly Robust for Variance Estimation?
Vanderbilt University Medical Center（范德堡大学医学中心）；UCSD Herbert Wertheim School of Public Health and Human Longevity Science（加州大学圣地亚哥分校赫伯特·韦特海姆公共卫生与人类长寿科学学院）；University of Florida（佛罗里达大学）
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Academy of Life and Natural Sciences, Xi’an Jiaotong-Liverpool University（西交利物浦大学生命与自然科学学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息研究院）
Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
Department of Mathematics and Physics, Gangneung-Wonju National University（数学与物理系，江陵-Wonju国立大学）；Department of Applied Statistics, Yonsei University（应用统计学系，延世大学）；Department of Statistics, Dongguk University（统计学系，东国大学）；Department of Marketing Bigdata, Mokwon University（市场营销大数据系，摩斯大学）；Department of Data Science, Gangneung-Wonju National University（数据科学系，江陵-Wonju国立大学）
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
University of Pittsburgh（匹兹堡大学）；University of Florida（佛罗里达大学）
