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聚类处理分配下的遗漏变量偏差敏感性分析AI 解读

Cinelli and Hazlett (2020) develops a sensitivity analysis method for the linear regression model that parameterizes omitted variable bias in terms of two partial $R^2$ parameters capturing the residual variation explained by an omitted confounder in the treatment and outcome respectively. This method is often applied to regressions fit to unit-level data when treatment is assigned at a higher level of aggregation -- as in clustered observational designs. This paper shows…

分期实施下三重差分回归的分解AI 解读

The triple-differences (TD) design is a popular identification strategy for causal effects in settings where researchers do not believe the parallel trends assumption of conventional difference-in-differences (DiD) is satisfied. TD designs augment the conventional 2x2 DiD with a "placebo" stratum -- observations that are nested in the same units and time periods but are known to be entirely unaffected by the treatment. However, many TD applications go beyond this simple…