Abstract In randomized trials in which two treatment arms are compared with a binary outcome, the causal effect can be identified by assuming that the two treatment arms are exchangeable. In trials with an ordinal outcome, which is categorized as more than two, the causal effect can be...

Abstract Manski (Monotone treatment response. Econometrica 1997;65:1311–34) and Manski and Pepper (Monotone instrumental variables: with an application to the returns to schooling. Econometrica 2000;68:997–1010) gave sharp bounds on causal effects under the assumptions of monotone treatment response...

Manski (Monotone treatment response. Econometrica 1997;65:1311–34) and Manski and Pepper (Monotone instrumental variables: with an application to the returns to schooling. Econometrica 2000;68:997–1010) gave sharp bounds on causal effects under the assumptions of monotone treatment response (MTR)...

Estimating the direct effect of a treatment on an outcome is often the focus of epidemiological and clinical research, when the treatment has more than one specified pathway to the defined outcome. Even if the total effect is unconfounded, the direct effect is not identified when unmeasured...

Noncompliance is a common concern in drawing causal inference in randomized trials. There is a large body of literature on methods for dealing with this issue. Specifically, it is well known that the average causal effect within the principal strata of compliance behavior (complier average...

In vaccine trials, the vaccination of one person might prevent the infection of another. This dependency makes it difficult to estimate the effect of a vaccine on infection. To deal with this issue, causal inference along with a principal stratification framework has been discussed....

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected...

Noncompliance with assigned treatment is an important problem of randomized clinical trials. In this situation, the structural mean model (SMM) approach focuses on the average treatment effect among patients actually treated (ATT). In contrast, the principal stratification (PS) approach...

Adjusting for an intermediate variable is a common analytic strategy for estimating a direct effect. Even if the total effect is unconfounded, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. This paper focuses on the application of the...