Abstract Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further...

In the case where non-experimental data are available from an industrial process and a directed graph for how various factors affect a response variable is known based on a substantive understanding of the process, we consider a problem in which a control plan involving multiple treatment...

Evaluating the performance of a medical diagnostic test is an important issue in disease diagnosis. Youden [<italic>Index for rating diagnostic tests</italic>, Cancer 3 (1950), pp. 32--35] stated that the ideal measure of performance is to ensure that the control group resembles the diseased group as closely as...

Evaluating the causal effect of an exposure on a response from case-control and cohort studies is a major concern in epidemiological and medical research. Since causal effects are in general nonidentifiable from such studies, this paper derives bounds on two causal measures: the causal risk...

We consider the problem of using data in studies with an unobserved treatment/response variable in order to evaluate average causal effects, when cause-effect relationships between variables can be described by a directed acyclic graph and the corresponding recursive factorization of a joint...

Consider a case where cause-effect relationships between variables can be described by a causal path diagram and the corresponding linear structural equation model. The paper proposes a graphical selection criterion for covariates to estimate the causal effect of a control plan. For designing...

Consider a case where cause-effect relationships between variables can be described as a directed acylic graph and the corresponding recursive factorization of a joint distribution. In order to provide the bounds on average causal effects in studies with a latent response variable, this paper...

This paper considers the problem of using observational data in the presence of selection bias to identify causal effects in the framework of linear structural equation models. We propose a criterion for testing whether or not observed statistical dependencies among variables are generated by...