11 citations · 25 across the 18 of their papers we have counts for
22 papers
Modified treatment policies that depend on the natural history of treatment
Iván Díaz, Nicholas T. Williams, Paweł Morzywołek +1
Longitudinal modified treatment policies (LMTP) are a class of interventions that allow the definition, identification, and estimation of causal effects in general settings, such a…
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Nicholas Williams, Alejandro Schuler
Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stabili…
crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
Richard Liu, Nicholas T. Williams, Kara E. Rudolph +1
Causal mediation analysis is widely used to investigate how causal effects operate through specific pathways linking treatments or exposures to outcomes. Recently, \texttt{crumble}…
Everything all at once: On choosing an estimand for multi-component environmental exposures
Kara E. Rudolph, Shodai Inose, Nicholas Williams +4
Many research questions -- particularly those in environmental health -- do not involve binary exposures. In environmental epidemiology, this includes multivariate exposure mixture…
Riesz representers for the rest of us
Nicholas T. Williams, Oliver J. Hines, Kara E. Rudolph
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This liter…
Longitudinal weighted and trimmed treatment effects with flip interventions
Alec McClean, Alexander W. Levis, Nicholas Williams +1
Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily g…
General targeted machine learning for modern causal mediation analysis
Richard Liu, Nicholas T. Williams, Kara E. Rudolph +1
Causal mediation analyses investigate the mechanisms through which causes exert their effects, and are therefore central to scientific progress. The literature on the non-parametri…
Longitudinal Generalizations of the Average Treatment Effect on the Treated for Multi-valued and Continuous Treatments
Herbert Susmann, Nicholas T. Williams, Kara E. Rudolph +1
The Average Treatment Effect on the Treated (ATT) is a common causal parameter defined as the average effect of a binary treatment among the subset of the population receiving trea…
Associations between pain-management treatments and opioid use disorder risk among Medicaid patients
Kara E. Rudolph, Nicholas T. Williams, Ivan Diaz +7
Introduction: Chronic pain patients are at increased risk of opioid-misuse. Less is known about the unique risk conferred by each pain-management treatment, as treatments are typic…
A drug classification pipeline for Medicaid claims using RxNorm
Nicholas Williams, Kara E. Rudolph
Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research. Materials and Metho…
Identification and estimation of mediational effects of longitudinal modified treatment policies
Brian Gilbert, Katherine L. Hoffman, Nicholas Williams +3
We demonstrate a comprehensive semiparametric approach to causal mediation analysis, addressing the complexities inherent in settings with longitudinal and continuous treatments, c…
Learning Optimal Dynamic Treatment Regimes from Longitudinal Data
Nicholas T. Williams, Katherine L. Hoffman Iván Díaz, Kara E. Rudolph
Studies often report estimates of the average treatment effect. While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect…
Recanting twins: addressing intermediate confounding in mediation analysis
Tat-Thang Vo, Nicholas Williams, Richard Liu +2
The presence of intermediate confounders, also called recanting witnesses, is a fundamental challenge to the investigation of causal mechanisms in mediation analysis, preventing th…
Two-Step Targeted Minimum-Loss Based Estimation for Non-Negative Two-Part Outcomes
Nicholas T. Williams, Richard Liu, Katherine L. Hoffman +3
Non-negative two-part outcomes are defined as outcomes with a density function that have a zero point mass but are otherwise positive. Examples, such as healthcare expenditure and…
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…
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…
Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19
Nicholas Williams, Michael Rosenblum, Iván Díaz
The rapid finding of effective therapeutics requires the efficient use of available resources in clinical trials. The use of covariate adjustment can yield statistical estimates wi…
Non-parametric causal effects based on longitudinal modified treatment policies
Iván Díaz, Nicholas Williams, Katherine L. Hoffman +1
Most causal inference methods consider counterfactual variables under interventions that set the treatment deterministically. With continuous or multi-valued treatments or exposure…