11 citations · 25 across the 18 of their papers we have counts for
3 papers · 1 filter
All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples
Kara E. Rudolph, Nicholas Williams, Caleb H. Miles +2
There is a long-standing debate in the statistical, epidemiological and econometric fields as to whether nonparametric estimation that uses data-adaptive methods, like machine lear…
Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders
Kara E Rudolph, Nicholas Williams, Ivan Diaz
Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful imp…
A novel decomposition to explain heterogeneity in observational and randomized studies of causality
Brian Gilbert, Ivan Dıaz, Kara E. Rudolph +2
This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized se…