Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and post-exposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms, and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, including if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits non-additive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no-treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called Universal Difference-in-differences (UDiD). Both fully parametric and more robust semiparametric UDiD estimators are described and illustrated in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil.

翻译：差分中的差异无疑是观察性（即非随机化）研究中最广泛用于评估干预因果效应的方法之一。该方法通常用于可获得暴露前后结局测量值，且可合理假设未观测混杂因素与结局的关联在两组暴露组中具有相同的绝对幅度，且随时间恒定的场景，即所谓的平行趋势假设。在实际应用中，当结局为二分类、计数或多分类变量，或未控制混杂因素对结局分布表现出非加性效应（即使效应随时间恒定）时，平行趋势假设可能不可信。我们提出一种替代方法，用比值比等混杂假设取代平行趋势假设。该假设下，治疗与无治疗状态下潜在结局之间的关联可通过一个将暴露前结局与暴露关联的合理设定广义线性模型加以识别。由于该新方法能够识别任何在无混杂偏倚情况下可被识别的因果效应（包括分位数处理效应等非线性效应），因此被恰当地称为通用差分中的差异（UDiD）。本文描述了完全参数化和更具鲁棒性的半参数UDiD估计量，并通过实际案例（关于巴西寨卡病毒爆发对出生率的因果效应）进行验证。