Specifications that impose constant treatment effects are common but biased, while fully flexible alternatives can be imprecise or infeasible. Under a bound on treatment effect heterogeneity, we propose a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence intervals (CIs). The ridge penalty is chosen to optimally trade off worst-case bias and variance in a Gaussian homoskedastic setting; the resulting CIs remain tight more generally and are valid even under lack of overlap. Varying the bound enables sensitivity analysis to departures from constant effects, which we illustrate in leading empirical applications of unconfoundedness and staggered adoption designs.

翻译：施加恒定治疗效果假设的规范虽然常见但存在偏差，而完全灵活的替代方案可能不精确或不可行。在治疗效果异质性有界约束下，我们提出了一种广义岭估计量$\texttt{regulaTE}$，能够生成异质性感知置信区间。该岭惩罚项旨在高斯同方差设定下最优权衡最坏情形偏差与方差；由此得到的置信区间在更一般情形下仍保持紧凑性，且即便在重叠性缺失条件下依然有效。通过改变异质性边界，可对常数效应假设的偏离进行敏感性分析——我们通过无混杂性设计与交错采用设计中的典型案例对此加以阐释。