Difference-in-differences is without a doubt the most widely used method for evaluating the causal effect of a hypothetical intervention in the possible presence of confounding bias due to hidden factors. The approach is typically used when both pre- and post-exposure outcome measurements are available, and one can reasonably assume that the additive association of the unobserved confounder with the outcome is equal in the two exposure arms, and 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, and more generally, when the unmeasured 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, which states that confounding bias for the causal effect of interest, encoded by an association between treatment and the potential outcome under no-treatment can be identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. As 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：差分法无疑是评估假设干预在可能存在隐藏因素导致的混杂偏倚时的因果效应最广泛使用的方法。该方法通常适用于存在暴露前和暴露后结局测量值的情况，且可合理假设未观测混杂因子与结局的加性关联在两组暴露组中相等且随时间恒定——即所谓的平行趋势假设。然而在许多实际场景中，平行趋势假设可能不可信，包括结局为二分类、计数或多分类变量时，更一般地，当未测量混杂因子对结局分布呈现非加性效应时（即使这些效应随时间恒定）。本文提出一种替代方法，用比值比等混杂假设替代平行趋势假设，该假设认为：通过建立暴露前结局与暴露之间的广义线性模型，可识别以治疗与未治疗条件下潜在结局之间关联表征的因果效应混杂偏倚。由于所提方法能识别任何在无混杂偏倚下可识别的因果效应（包括分位数治疗效应等非线性效应），该方法被恰当地称为通用差分法（UDiD）。本文描述了全参数化与更稳健的半参数化UDiD估计量，并通过巴西寨卡病毒暴发对出生率因果效应的实际应用案例进行验证。