超越预趋势：基于不一致性的双重差分敏感性分析
Beyond Pretrends: A Discordance-Based Sensitivity Analysis for Difference-in-Differences
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中文总结 AI 辅助
本文提出基于不一致性的双重差分敏感性分析模型，以处理平行趋势假设违背，通过插补不一致性评估敏感性，并应用于南非案例。
中文摘要 AI 辅助
在经典的双重差分设计中，对照组在干预后的变化被用作处理组同期反事实变化的插补，这一插补以平行趋势为前提。然而，组间构成的差异可能导致各组结果随时间演变的方式存在差异，从而使该插补易受混杂因素影响。另一种插补——例如基于处理组干预前变化的插补——避免了这种组间混杂，但引入了组内时间变化带来的混杂风险。理想情况下，这两种插补各自易受不同来源的混杂影响，其值应当一致，从而得出相同的因果结论。当插补不一致时，平行趋势下的结论更关键地依赖于该假设，因为替代插补会指向不同的结果。然而，在这些情形下，现有的基于预趋势的敏感性分析可能表现出较低的敏感性，因为它们忽略了对照组中相对于预趋势的干预后偏离。因此，本文提出一种基于不一致性的敏感性模型，在该模型中，平行预趋势是低敏感性的必要条件而非充分条件。我根据平行趋势下和替代假设下ATT之间的期望距离（以这些假设的联合合理性加权）正式论证了该模型。随后，我提供了决策理论依据，利用平行趋势插补与替代插补之间的最坏情况不一致性来基准化对平行趋势的违背。最后，我应用基于预趋势和基于不一致性的敏感性模型，评估劳动供给冲击如何影响南非种族隔离时期政策选举支持，展示两种方法如何产生不同结果。
英文摘要
In the canonical Difference-in-Differences design, the control group's post-treatment change serves as an imputation of the treated group's counterfactual change in the same period, an imputation justified by parallel trends. However, differences in group composition can produce between-group differences in how outcomes would evolve over time, rendering this imputation vulnerable to confounding. An alternative imputation -- such as one based on the treated group's pre-treatment change -- avoids such between-group confounding but introduces the risk of confounding from within-group temporal shifts. Ideally, both imputations, each vulnerable to different sources of confounding, would have concordant values, thereby yielding the same causal conclusions. When the imputations are discordant, conclusions under parallel trends hinge more critically on that assumption since alternative imputations would point to different results. Yet in these scenarios, existing pretrends-based sensitivity analyses can show low sensitivity because they ignore post-treatment deviations from pretrends in the control group. This paper therefore proposes a discordance-based sensitivity model in which parallel pretrends are necessary but not sufficient for low sensitivity. I formally justify this model in terms of the expected distance between the ATT under parallel trends and under alternative assumptions, weighted by the joint plausibility of those assumptions. I then provide a decision-theoretic rationale for benchmarking violations of parallel trends using the worst-case discordance between the parallel trends imputation and alternative imputations. Finally, I apply both pretrends- and discordance-based sensitivity models to assess how a labor supply shock influenced electoral support for apartheid-era policies in South Africa, showing how the two approaches yield different results.
发表机构
- Baruch College, City University of New York (CUNY)（纽约市立大学巴鲁克学院）
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