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.

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