标题： 使用Riesz表示的双重差分模型中处理效应的敏感性分析 显示英文标题

标题： Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation

摘要： 差分法（DiD）是经济学、政治科学等领域的实证研究中最受欢迎的方法之一。 这些模型的识别基于条件平行趋势假设：在没有处理的情况下，根据事前协变量，处理组和未处理组的平均结果被假定随时间平行演变。 我们引入了一种新的DiD模型敏感性分析方法，该方法评估由于不可观测混杂因素导致这一假设违反时DiD估计的稳健性，使研究人员能够透明地评估和传达其因果估计结果的可信度。 我们的方法专注于通过双重机器学习进行估计，并扩展了基于横截面设置中Riesz表示的先前敏感性分析工作。 我们在典型的$2\times2$设置中建立了点估计和置信区间的渐近边界，以及在交错处理采用设置中的组-时间因果参数。 我们的方法使得可以将平行趋势违反的表述与来自（1）预测试、（2）协变量基准比较和（3）标准报告统计量及可视化结果的实证证据联系起来。 我们提供了广泛的模拟实验，证明了我们敏感性方法和诊断的有效性，并将我们的方法应用于两个实证应用。 显示英文摘要

摘要： Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, conditional on pre-treatment covariates. We introduce a novel approach to sensitivity analysis for DiD models that assesses the robustness of DiD estimates to violations of this assumption due to unobservable confounders, allowing researchers to transparently assess and communicate the credibility of their causal estimation results. Our method focuses on estimation by Double Machine Learning and extends previous work on sensitivity analysis based on Riesz Representation in cross-sectional settings. We establish asymptotic bounds for point estimates and confidence intervals in the canonical $2\times2$ setting and group-time causal parameters in settings with staggered treatment adoption. Our approach makes it possible to relate the formulation of parallel trends violation to empirical evidence from (1) pre-testing, (2) covariate benchmarking and (3) standard reporting statistics and visualizations. We provide extensive simulation experiments demonstrating the validity of our sensitivity approach and diagnostics and apply our approach to two empirical applications.