Estimating causal effects of treatments in randomized and nonrandomized studies

Presents a discussion of the conditions under which causal inferences can be made from data collected in randomized and nonrandomized studies. Defines a causal effect as the comparison of potential outcomes under treatment and control for the same unit. Introduces the assignment mechanism as central to valid causal inference and discusses matching and covariance adjustment as methods for nonrandomized studies.