59 citations · 109 across the 16 of their papers we have counts for
4 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…
Efficient and flexible estimation of natural mediation effects under intermediate confounding and monotonicity constraints
Kara E. Rudolph, Ivan Diaz
Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a tre…