标题： 什么使治疗效应可识别？ 超越无混淆性的表征和估计量 显示英文标题

标题： What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness

摘要： 大多数在因果推断中广泛使用的平均处理效应（ATE）估计量都依赖于无混杂性和重叠性的假设。 无混杂性要求观察到的协变量能够解释结果和处理之间的所有相关性。 重叠性要求所有个体的处理决策中存在随机性。 然而，许多类型的研究经常违反无混杂性或重叠性，例如具有确定性处理决策的观察性研究——通常被称为回归不连续设计——会违反重叠性。 在本文中，我们开始研究一般条件，这些条件能够使平均处理效应的识别成为可能，超越了无混杂性和重叠性。 具体来说，遵循统计学习理论的范式，我们提供了一个可解释的条件，该条件对于ATE的识别是充分且必要的。 此外，这一条件还描述了处理组上的平均处理效应（ATT）的识别，并且也可以用来描述其他处理效应。 为了说明我们条件的实用性，我们展示了几个被广泛研究的情况，在这些情况下我们的条件得到满足，因此我们证明了在先前工作无法捕捉的领域中可以识别ATE。 例如，在对数据分布的轻微假设下，这适用于Tan（2006）和Rosenbaum（2002）提出的模型，以及Thistlethwaite和Campbell（1960）引入的回归不连续设计模型。 对于每种情况，我们还表明，在自然的额外假设下，可以从有限样本中估计ATE。 我们认为这些发现为将学习理论见解与因果推断方法相结合开辟了新的途径，特别是在具有复杂处理机制的观察性研究中。 显示英文摘要

摘要： Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that the observed covariates account for all correlations between the outcome and treatment. Overlap requires the existence of randomness in treatment decisions for all individuals. Nevertheless, many types of studies frequently violate unconfoundedness or overlap, for instance, observational studies with deterministic treatment decisions - popularly known as Regression Discontinuity designs - violate overlap. In this paper, we initiate the study of general conditions that enable the identification of the average treatment effect, extending beyond unconfoundedness and overlap. In particular, following the paradigm of statistical learning theory, we provide an interpretable condition that is sufficient and necessary for the identification of ATE. Moreover, this condition also characterizes the identification of the average treatment effect on the treated (ATT) and can be used to characterize other treatment effects as well. To illustrate the utility of our condition, we present several well-studied scenarios where our condition is satisfied and, hence, we prove that ATE can be identified in regimes that prior works could not capture. For example, under mild assumptions on the data distributions, this holds for the models proposed by Tan (2006) and Rosenbaum (2002), and the Regression Discontinuity design model introduced by Thistlethwaite and Campbell (1960). For each of these scenarios, we also show that, under natural additional assumptions, ATE can be estimated from finite samples. We believe these findings open new avenues for bridging learning-theoretic insights and causal inference methodologies, particularly in observational studies with complex treatment mechanisms.