A randomized trial allows estimation of the causal effect of an intervention compared to a
control in the overall population and in subpopulations defined by baseline characteristics …

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 …

Identification of Causal Effects Within Principal Strata Using Auxiliary Variables Page 1 Statistical
Science 2021, Vol. 36, No. 4, 493–508 https://doi.org/10.1214/20-STS810 © Institute of …

In causal inference studies, interest often lies in understanding the mechanisms through
which a treatment affects an outcome. One approach is principal stratification (PS), which …

Causal inference is often complicated by post-treatment variables, which appear in many
scientific problems, including noncompliance, truncation by death, mediation, and surrogate …

This article revisits results from a field experiment, conducted in Florence, Italy, to study the
effects of incentives provided to high school teens to motivate them to visit art museums. In …

Even in the best-designed experiment, noncompliance can complicate analysis. While the
intent-to-treat effect remains identified, randomization alone no longer identifies the complier …

Mediation analysis is widely used for investigating direct and indirect causal pathways
through which an effect arises. However, many mediation analysis studies are challenged …

Randomized evaluations of educational technology produce log data as a bi-product: highly
granular data on student and teacher usage. These datasets could shed light on causal …