Many empirical questions in economics and other social sciences depend on causal effects
of programs or policies. In the last two decades, much research has been done on the …

This report calls on the nation—its leaders, its mental health research and service provision
agencies, its schools, its primary care medical systems, its community-based organizations …

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are
commonly interpreted as estimating positively weighted averages of causal effects, referred …

In this essay I discuss potential outcome and graphical approaches to causality, and their
relevance for empirical work in economics. I review some of the work on directed acyclic …

A goal of many health studies is to determine the causal effect of a treatment or intervention
on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly …

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The
topic of missing data has gained considerable attention in recent decades. This new edition …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …

Many scientific problems require that treatment comparisons be adjusted for posttreatment
variables, but the estimands underlying standard methods are not causal effects. To address …

Instrumental variable (IV) methods are widely used to identify causal effects in models with
endogenous explanatory variables. Often the instrument exclusion restriction that underlies …