A review of epidemiological papers conducted in 2009 concluded that several studies
employed variable selection methods susceptible to introduce bias and yield inadequate …

Abstract Purpose of Review Propensity score methods have become commonplace in
pharmacoepidemiology over the past decade. Their adoption has confronted formidable …

Instrumental variables are widely used for estimating causal effects in the presence of
unmeasured confounding. Under the standard instrumental variable model, however, the …

The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment
can be improved by including in the treatment and outcome models only those covariates …

More than 1100 abandoned mines, milling sites and waste piles from the uranium mining
period are scattered across the Navajo Nation, resulting in exposures to environmental …

Background The change in estimate is a popular approach for selecting confounders in
epidemiology. It is recommended in epidemiologic textbooks and articles over significance …

Valid estimation of treatment effects from observational data requires proper control of
confounding. If the number of covariates is large relative to the number of observations, then …

Spurred on by recent successes in causal inference competitions, Bayesian nonparametric
(and high‐dimensional) methods have recently seen increased attention in the causal …

This paper investigates the appropriateness of the integration of flexible propensity score
modeling (nonparametric or machine learning approaches) in semiparametric models for …

This article discusses the fundamental principles of causal inference–the area of statistics
that estimates the effect of specific occurrences, treatments, interventions, and exposures on …