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 …

Compared to what? Variation in the impacts of early childhood education by alternative care
type Page 1 The Annals of Applied Statistics 2016, Vol. 10, No. 3, 1245–1285 DOI: 10.1214/16-AOAS910 …

Practitioners are interested in not only the average causal effect of a treatment on the
outcome but also the underlying causal mechanism in the presence of an intermediate …

Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-
day lives, humans still make consequential decisions. While the existing literature focuses …

Causal inference concerns not only the average effect of the treatment on the outcome but
also the underlying mechanism through an intermediate variable of interest. Principal …

Background In cluster randomized trials, patients are typically recruited after clusters are
randomized, and the recruiters and patients may not be blinded to the assignment. This …

Researchers addressing posttreatment complications in randomized trials often turn to
principal stratification to define relevant assumptions and quantities of interest. One …

This article discusses the fundamental principles of causal inference–the area of statistics
that estimates the effect of specific occurrences, treatments, interventions, and exposures on …

We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of
mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part …

Supplement to “Evaluating the causal effect of university grants on student dropout:
Evidence from a regression discontinuity design using principal stratification”. We describe …