In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Inferring causal effects of treatments is a central goal in many disciplines. The potential
outcomes framework is a main statistical approach to causal inference, in which a causal …

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 …

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 …

Principal stratification is a causal framework to analyse randomized experiments with a post-
treatment variable between the treatment and end point variables. Because the principal …

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

Unless strong assumptions are made, nonparametric identification of principal causal effects
can only be partial and bounds (or sets) for the causal effects are established. In the …

This chapter provides an overview of the econometric and statistical methods for drawing
inference on causal effects from randomized experiments under the potential outcome …

Nonignorable missing data poses key challenges for estimating treatment effects because
the substantive model may not be identifiable without imposing further assumptions. For …

Frequently, empirical studies are plagued with missing data. When the data are missing not
at random, the parameter of interest is not identifiable in general. Without additional …