Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States.
As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the \textit{entire} conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.
Wuhan University of Science and Technology（武汉科技大学）；College of Science（理学院）；Intelligent Automobile Engineering Research Institute（智能汽车工程研究院）；School of Automobile and Traffic Engineering（汽车与交通工程学院）
Advancing Counterfactual Inference through Nonlinear Quantile Regression
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The University of Sydney（悉尼大学）
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
Institute of Service Science, National Tsing Hua University（服务科学研究所，国立清华大学）；Faculty of Business and Economics, University of Antwerp（商学院与经济学院，安特卫普大学）；Copenhagen Business School（哥本哈根商学院）
Proximal Causal Inference for Marginal Counterfactual Survival Curves
对比治疗组间的边缘反事实生存曲线是推断干预对右删失至事件时间结局的因果影响的有效且流行的方法。在观察环境中得出此类推论的一个关键挑战是可能存在未测量的混杂，这可能使假设没有隐藏混杂偏倚的最常用方法无效。在本文中，我们没有做出标准的无不可测量混杂假设，而是扩展了Miao et al.（2018），Tchetgen et al.（2020），Cui et al.（2020）最近提出的近端因果推断框架，以通过利用观察到的协变量作为不可测量混杂因素的不完美代理来获得因果生存对比的非参数识别。具体来说，我们开发了一个近端逆概率加权（PIPW）估计，标准IPW的近端模拟，它允许观察到的数据分布的时间到事件的结果，以保持完全不受限制。PIPW估计依赖于所谓的治疗混杂桥接函数的参数模型，该桥接函数将治疗过程与混杂代理相关联。因此，PIPW可能对模型错误指定敏感。为了提高稳健性和效率，我们还提出了一个近似双稳健估计，并建立了一致相合性和渐近正态性的估计。我们进行了广泛的模拟，以检查我们的估计有限样本的性能，并提出的方法应用于一项研究，评估重症监护室的重症患者的右心导管插入术的有效性。
On Predictive planning and counterfactual learning in active inference
Turner Institute for Brain and Mental Health（特纳脑与心理健康研究所）；School of Psychological Sciences（心理科学学院）；Monash University（莫纳什大学）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什大学研究院）；IIT Bombay（印度理工学院孟买分校）；RIKEN Center for Brain Science（理化学研究所脑科学中心）；University College London（伦敦大学学院）；Wellcome Trust Centre for Human Neuroimaging（惠康信托人类神经影像中心）；CIFAR（加拿大高级研究所）
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
University of Georgia（佐治亚大学）；Beijing Normal University（北京师范大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Shanghai Jiao Tong University（上海交通大学）；The University of Virginia（弗吉尼亚大学）
