Machine learning algorithms are becoming ubiquitous in modern life. When used to help inform human decision making, they have been criticized by some for insufficient accuracy, an absence of transparency, and unfairness. Many of these concerns can be legitimate, although they are less convincing...

Abstract In causal mediation analysis, nonparametric identification of the natural indirect effect typically relies on, in addition to no unobserved pre-exposure confounding, fundamental assumptions of (i) so-called “cross-world-counterfactuals” independence and (ii) no exposure-induced...

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 Unobserved confounding is a well-known threat to causal inference in non-experimental studies. The instrumental variable design can under certain conditions be used to recover an unbiased estimator of a treatment effect even if unobserved confounding cannot be ruled out with certainty....

Semiparametric methods have been developed to increase efficiency of inferences in randomized trials by incorporating baseline covariates. Locally efficient estimators of marginal treatment effects, which achieve minimum variance under an assumed model, are available for settings in which...

The risk ratio effect measure is often the main parameter of interest in epidemiologic studies with a binary outcome. In this paper, the author presents a simple and efficient two-stage approach to estimate the risk ratios directly, which does not directly rely on consistency for an estimate of...

We describe a novel approach to nonparametric point and interval estimation of a treatment effect in the presence of many continuous confounders. We show that the problem can be reduced to that of point and interval estimation of the expected conditional covariance between treatment and response...