Policy-relevant treatment effect estimation in a marketplace setting requires assessing both the direct treatment benefit and spillovers induced by changes to the market equilibrium. We show how to identify and estimate policy-relevant treatment effects using a unit-randomized trial run within a single large market. A Bernoulli-randomized trial allows consistent estimation of direct effects and of treatment-heterogeneity measures that enable welfare-improving targeting. Estimating spillovers—and providing confidence intervals for the direct effect—requires estimates of price elasticities, which we provide using an augmented experimental design. We illustrate our results using a simulation calibrated to a conditional cash-transfer experiment in the Philippines.

There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference-in-differences and synthetic control methods. Relative to these methods we find, both theoretically and empirically, that this “synthetic difference-in-differences” estimator has desirable robustness properties, and that it performs well in settings where the conventional estimators are commonly used in practice. We study the asymptotic behavior of the estimator when the systematic part of the outcome model includes latent unit factors interacted with latent time factors, and we present conditions for consistency and asymptotic normality.