Generalizing Difference-in-Differences to Non-Canonical Settings: Identifying an Array of Estimands
将双重差分推广至非典型情形：识别多种因果估计对象AI 解读 ↓
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将双重差分推广至非典型情形：识别多种因果估计对象
- 研究问题
- 在两个组、两个时期但处理安排不同于经典双重差分的情况下，组间变化之差能在何种平行趋势假设下识别有意义的因果量？
- 方法与思路
- 作者系统组合组别、时期与处理状态，以及不同处理制度下的平行趋势假设，并借助单世界干预图讨论假设的结构依据。
- 材料陈述的贡献
- 摘要称，论文厘清组间双重差分公式在不同数据结构与假设组合下识别的因果对象，并讨论平行趋势何时可接受经验检验。
- 阅读关注点
- 应逐项核对处理状态组合、潜在结果制度与对应因果对象，特别关注经验检验需要哪些额外时期、观测信息或结构条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出完整识别组合，不能认为所有前后比较均有因果解释。
原文摘要
Consider a general setting in which data on an outcome is collected in two `groups' at two time periods, with certain group-periods deemed `treated' and others `untreated'. A special case is the canonical Difference-in-Differences (DiD) setting in which one group is treated only in the second period while the other is treated in neither period. Then it is well known that under a parallel trends assumption across the two groups the classic DiD formula (subtracting the average change in outcome across periods in the treated group by the average change in the outcome across periods in the untreated group) identifies the average treatment effect on the treated in the second period. But other relations between group, period, and treatment are possible. For example, the groups might be demographic (or other baseline covariate) categories with all units in both groups treated in the second period and none treated in the first, i.e. a pre-post design. Or one group might be treated in both periods while the other is treated in neither. Furthermore, other parallel trends assumptions under other treatment regimes are possible. For example, we could assume the two groups' potential outcomes would evolve in parallel under a regime of `do not switch treatment in the second period'. In fact, there is a literal array of data structures and parallel trends assumptions. The difference between the changes in outcomes of the two groups, which we dub the `group DiD' (gDiD) formula, identifies different causal estimands depending on the data structure and parallel trends assumption adopted. Here, we determine under which combinations of data structure and assumptions the gDiD formula identifies meaningful causal estimands. We also explore when parallel trends assumptions are amenable to empirical check or structural justification via Single World Intervention Graphs.