59 citations · 109 across the 16 of their papers we have counts for
16 papers
From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity
Michael Cheung, Candus Shi, Kara E Rudolph +3
Identifying heterogeneous populations across which exposure effects vary is essential for transportability applications, cost-benefit analyses, and intervention prioritization. Tra…
Studying continuous, time-varying, and/or complex exposures using longitudinal modified treatment policies
Katherine L. Hoffman, Diego Salazar-Barreto, Nicholas Williams +2
This tutorial discusses methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and…
Improving efficiency in transporting average treatment effects
Kara E. Rudolph, Nicholas T. Williams, Elizabeth A. Stuart +1
We develop flexible, semiparametric estimators of the average treatment effect (ATE) transported to a new population ("target population") that offer potential efficiency gains. Tr…
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…
Causal mediation with instrumental variables
Kara E. Rudolph, Nicholas Williams, Ivan Diaz
Mediation analysis is a strategy for understanding the mechanisms by which treatments or interventions affect later outcomes. Mediation analysis is frequently applied in randomized…
When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws
Kara E. Rudolph, Catherine Gimbrone, Ellicott C. Matthay +4
Violations of the positivity assumption (also called the common support condition) challenge health policy research, and can result in significant bias, large variance, and invalid…
When the ends don't justify the means: Learning a treatment strategy to prevent harmful indirect effects
Kara E. Rudolph, Ivan Diaz
There is a growing literature on finding so-called optimal treatment rules, which are rules by which to assign treatment to individuals based on an individual's characteristics, su…
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects
Nima S. Hejazi, Kara E. Rudolph, Mark J. van der Laan +1
Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary treatments and static interventions, and (ii) dir…
Efficiently transporting causal (in)direct effects to new populations under intermediate confounding and with multiple mediators
Kara E. Rudolph, Ivan Diaz
The same intervention can produce different effects in different sites. Transport mediation estimators can estimate the extent to which such differences can be explained by differe…
Non-parametric efficient causal mediation with intermediate confounders
Iván Díaz, Nima S. Hejazi, Kara E. Rudolph +1
Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confoun…
Transporting stochastic direct and indirect effects to new populations
Kara E Rudolph, Jonathan Levy, Mark J van der Laan
Transported mediation effects may contribute to understanding how and why interventions may work differently when applied to new populations. However, we are not aware of any estim…
Complier stochastic direct effects: identification and robust estimation
Kara E Rudolph, Oleg Sofrygin, Mark J van der Laan
Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable (IV)-direct effect of…
Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting
Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng +1
Causal mediation analysis can improve understanding of the mechanisms underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation h…