With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative...

How do international institutions affect political liberalization in member states? Motivated by an examination of the World Bank loans program, we argue that institutions can confer prestige in exchange for political reforms. When offered an opportunity to improve their...

Community-driven reconstruction (CDR) is an approach to post-war reconstruction that gives discretion to local community councils in establishing priorities and overseeing the implementation of reconstruction and development activities. A series of methodologically exceptional studies has raised...

Abstract Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple,...

Abstract Much research has recently been devoted to understanding the effects of party incumbency following close elections, typically using a regression discontinuity design. Researchers have demonstrated that close elections in the US House of Representatives may systematically favor certain...

Abstract We derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then...

Abstract Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency. These inferential targets are sometimes data-adaptive, in that they are dependent on the empirical distribution of the data. In...

Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for...

Published reports of experiments with noncompliance often fail to report information necessary for recovering average potential outcomes for compliers. We derive sharp bounds on the average potential outcomes for compliers, when given only average outcomes for units assigned to treatment,...

Applying concepts from partial identification to the domain of finite population sampling, we propose a method for interval estimation of a population mean when the probabilities of sample selection lie within a posited interval. The interval estimate is derived from sharp bounds on the Hajek...