Estimating Treatment Effects Under Bounded Heterogeneity

Abstract

Specifications that impose constant treatment effects are common and can be biased under heterogeneity, whereas fully flexible alternatives can be imprecise or infeasible. Under a bound on the target-weighted variance of treatment effects, this paper proposes a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence intervals (CIs). The ridge penalty is chosen to minimize the heterogeneity-aware CI length in a homoskedastic Gaussian setting; the resulting CIs are asymptotically valid even when overlap fails. Varying the bound enables sensitivity analysis of departures from constant effects, illustrated in applications to unconfoundedness and staggered adoption designs.