When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

We apply causal forests to a dataset derived from the National Study of Learning Mindsets,
and discusses resulting practical and conceptual challenges. This note will appear in an …

To what extent do survey experimental treatment effect estimates generalize to other
populations and contexts? Survey experiments conducted on convenience samples have …

Abstract The Moving to Opportunity (MTO) experiment offered randomly selected families
housing vouchers to move from high-poverty housing projects to lower-poverty …

We develop new tools for estimating the causal effects of treatments or instruments that
combine multiple sources of variation according to a known formula. Examples include …

There are a number of available methods for selecting whom to prioritize for treatment,
including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We …

A bold re-examination of how political attitudes change in response to information. Many
mistakenly believe that it is fruitless to try to persuade those who disagree with them about …

The past decade has witnessed an explosion of interest in research and education in causal
inference, due to its wide applications in biomedical research, social sciences, artificial …

Education research has experienced a methodological renaissance over the past two
decades, with a new focus on large-scale randomized experiments. This wave of …

Survey experiments often yield intention-to-treat effects that are either statistically and/or
practically “non-significant.” There has been a commendable shift toward publishing such …