Statistical Analysis of Causal Mechanisms: Identification, Inference, and Sensitivity Analysis

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。因果推论是大多数社会科学研究的中心目标。近年来，为了提高因果结论的有效性，实证研究者越来越依赖于实验。与此同时，关于因果推理的方法论文献也蓬勃发展。尽管有了这两个发展，但实验面临着一个根本性的弱点，因为它们只提供了因果关系的黑箱观点。治疗的随机化使研究人员有可能获得治疗效果的有效估计，从而确定治疗是否因果地影响结果。然而，随机实验并没有提供太多关于带来这种因果效应的潜在因果机制的信息。他们没有回答治疗如何以及为什么会对结果产生因果影响的重要问题。这是一个重要的局限性，因为因果机制的识别通常需要检验对因果效应提供不同解释的相互竞争的理论的有效性。拟议的研究的目标是通过开发一套新的统计方法来分析因果机制来克服这一局限性。具体地说，研究者侧重于因果中介分析，通过估计直接和间接影响来检验因果机制。在这个框架中，通过检查位于治疗和结果变量之间的因果路径中的中间变量的作用来研究可供选择的因果解释。这项研究涉及因果调解分析的三个重要方面。首先，识别分析建立了确定直接和间接影响所需的最小假设集，并澄清了关于特定因果机制的数据提供信息的确切程度。其次，参数和非参数估计量的拟议发展将使从观测数据进行推断成为可能，并允许研究人员计算不确定性估计和点估计。第三，敏感度分析是因果调解分析的关键，它需要一个很强的识别性假设。拟议的敏感性分析方法将使应用研究人员能够检查他们的经验发现对于可能违反关键识别假设的稳健性。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Causal inference is the central goal of most social science research. In recent years, empirical researchers are increasingly relying on experiments in order to improve the validity of causal conclusions. At the same time, the methodological literature on causal inference has flourished. Despite these two developments, experiments confront a fundamental weakness since they only provide a black-box view of causality. The randomization of treatment makes it possible for researchers to obtain a valid estimate of treatment effect and thereby determine whether the treatment causally affects the outcome. However, randomized experiments do not provide much information about the underlying causal mechanisms that have brought about such causal effects. They do not answer the important questions of how and why the treatment causally affects the outcome. This is an important limitation because the identification of causal mechanisms is often required to test the validity of competing theories that offer different explanations about causal effects.The goal of the proposed research is to overcome this limitation by developing a set of new statistical methods for the analysis of causal mechanisms. Specifically, the investigator focuses on causal mediation analysis where causal mechanisms are examined by estimating direct and indirect effects. In this framework, alternative causal explanations are investigated by examining the roles of intermediate variables that lie in the causal path between the treatment and outcome variables. The proposed research addresses three important aspects of causal mediation analysis. First, identification analysis establishes a minimum set of assumptions that are required to ascertain direct and indirect effects and clarifies the exact degree to which the data are informative about particular causal mechanisms. Second, the proposed development of parametric and nonparametric estimators will make inference from the observed data possible and allow researchers to compute uncertainty estimates as well as point estimates. Third, sensitivity analysis is essential for causal mediation analysis which requires a strong identifying assumption. The proposed methods for sensitivity analysis will allow applied researchers to examine the robustness of their empirical findings to the possible violation of the key identifying assumption.