最近 Imai, Kosuke, In Song Kim, and Erik Wang. ``Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.'' American Journal of Political Science, Forthcoming, 可能是较一般目前论坛上大家用 (包括我自己) 的方法更周延 (但没有 Stata，只有 R code).
- Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open-source software package is available for implementing the proposed matching methods.
Google 的中文翻译：
- 匹配方法通过减少模型依赖性和提供直观的诊断来提高因果推理的有效性。虽然它们已成为跨学科标准工具包的一部分，但在分析时间序列横截面数据时很少使用匹配方法。我们填补了这一方法上的空白。在提议的方法中，我们首先将每个处理过的观察结果与来自同一时间段内的其他单元的控制观察值进行匹配，这些单元具有相同的处理历史，直到预先指定的滞后数。我们使用标准匹配和加权方法来进一步细化这个匹配集，以便处理和匹配的控制观察具有相似的协变量值。通过检查协变量平衡来评估匹配的质量。最后，我们使用差分中的差分估计量估计短期和长期平均治疗效果，考虑时间趋势。我们通过模拟和实证研究来说明所提出的方法。一个开源 R 软件包可用于实现建议的匹配方法。
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