Abstract The paper considers the properties of and relations between confounding and effect modification from the perspective of causal inference and with a distinction drawn as to how each of these two epidemiologic concepts can be defined both with respect to a distribution of potential...

Abstract Mediation analysis is widely adopted to infer causal mechanism by disentangling indirect or mediated effects of an exposure on an outcome through given intermediaries, from the remaining direct effect. Traditional approaches build on standard regression models for the outcome and...

Abstract The between-within (BW) model is a popular regression model for twin data. Despite its popularity, the properties of the BW model have not yet been thoroughly investigated, and most reviews are largely heuristic. The aim of this paper is to provide a formal guide to the causal...

Abstract For decades, social scientists have been trying to answer causal questions about the effectiveness of certain programs or policies. The conventional methodology for answering such causal questions relies on the “no interference between different units” assumption; that is, a...

Abstract Robins et al. (2004 , Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva: World Health Organization) introduced the extended g-formula to estimate from observational data the risk of failure under...

Abstract In a recent article, VanderWeele and Vansteelandt (American Journal of Epidemiology, 2010, 172:1339–1348) (hereafter VWV) build on results due to Judea Pearl on causal mediation analysis and derive simple closed-form expressions for so-called natural direct and indirect effects in an...

Abstract Matched pairs appear frequently in studies of the association between a dichotomous exposure and a dichotomous outcome. The standard conditional logistic regression estimator of the common odds ratio is consistent, but uses only information from the doubly discordant pairs. To gain...

Abstract In this work, we propose a different “surgical modified model” for the construction of counterfactual variables under non-parametric structural equation models. This approach allows the simultaneous representation of counterfactual responses and observed treatment assignment, at...

Abstract Young, Hernán, and Robins consider the mean outcome under a dynamic intervention that may rely on the natural value of treatment. They first identify this value with a statistical target parameter, and then show that this statistical target parameter can also be identified with a...

Abstract Many of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new...