After treatment is found to be effective in a clinical study, attention often focuses on the effect of treatment duration on outcome. Such an analysis facilitates recommendations on the most beneficial treatment duration. In many studies, treatment duration is left to the discretion of the...

Studies of large policy interventions typically do not involve randomization. Adjustments, such as matching, can remove the bias due to observed covariates, but residual confounding remains a concern. In this paper we introduce two analytical strategies to bolster inferences of the effectiveness...

Past research on the associations between area-level/contextual social capital and health has produced conflicting evidence. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual...

Many inquiries regarding the causal effects of policies or programs are based on research designs where the treatment assignment process is unknown, and thus valid inferences depend on tenuous assumptions about the assignment mechanism. This article draws attention to the importance of...

In many empirical problems, the evaluation of treatment effects is complicated by sample selection such that the outcome is only observed for a non-random subpopulation. In the absence of instruments and/or tight parametric assumptions, treatment effects are not point identified, but can be...

The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on his or her behavior...

This survey gives a brief overview of the literature on the difference-in-difference (DiD) estimation strategy and discusses major issues using a treatment effects perspective. In this sense, this survey gives a somewhat different view on DiD than the standard textbook discussion of the DiD...

Instrumental variables can be used to make inferences about causal effects in the presence of unmeasured confounding. For a model in which the instrument, intermediate/treatment, and outcome variables are all binary, Balke and Pearl (1997, Journal of the American Statistical Association 92:...