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通过统一半参数双重差分方法估计处理效应AI 解读

Difference-in-differences (DID) approaches are widely used for estimating causal effects with observational data before and after an intervention. DID traditionally estimates the average treatment effect among the treated after making a parallel trends assumption on the means of the outcome. With skewed outcomes, a transformation is often needed; however, the transformation may be difficult to choose, results may be sensitive to the choice, and parallel trends assumptions…

理解错位实施政策的双重差分：Medicaid 与 HIV 的应用AI 解读

While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for observational data to explore causal relationships. Difference-in-differences (DID) is one such method that, most commonly, estimates a difference in some mean outcome in a group before and after the implementation of an…

内生用药下纵向生物标志物年龄趋势的半参数估计AI 解读

In cohort studies, non-random medication use can pose barriers to estimation of the natural history trend in a mean biomarker value (namely, the association between a predictor of interest and a biomarker outcome that would be observed in the absence of biomarker-specific treatment). Common causes of treatment and outcomes are often unmeasured, obscuring our ability to easily account for medication use with commonly invoked assumptions such as ignorability. Further, absent…