Abstract Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately...

Abstract Large randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study...

Abstract Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity...

Abstract Propensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological...

Abstract: In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance...

Abstract: While standard meta-analysis pools the results from randomized trials that compare two treatments, network meta-analysis aggregates the results of randomized trials comparing a wider variety of treatment options. However, it is unclear whether the aggregation of effect estimates across...

Abstract In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in...

Abstract The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have...

Abstract Estimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through the use of fully adjusted outcome...