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10/08/2022
Doubly robust estimation and sensitivity analysis for marginal structural quantile models
The marginal structure quantile model (MSQM) is a useful tool to charact...
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06/16/2022
A new tool for clustered survival data and multiple treatments: Estimation of treatment effect heterogeneity and variable selection
A new tool, riAFT-BART, was recently developed to draw causal inferences...
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02/16/2022
A flexible approach for causal inference with multiple treatments and clustered survival outcomes
When drawing causal inferences about the effects of multiple treatments ...
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10/19/2021
CIMTx: An R package for causal inference with multiple treatments using observational data
CIMTx provides efficient and unified functions to implement modern metho...
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09/27/2021
Joint marginal structural models to estimate the causal effects of multiple longitudinal treatments in continuous time with application to COVID-19
To draw real-world evidence about the comparative effectiveness of compl...
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08/05/2021
Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho
Hahn et al. (2020) offers an extensive study to explicate and evaluate t...
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07/20/2021
Strategies for variable selection in large-scale healthcare database studies with missing covariate and outcome data
Prior work has shown that combining bootstrap imputation with tree-based...
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04/06/2021
Variable selection with missing data in both covariates and outcomes: Imputation and machine learning
The missing data issue is ubiquitous in health studies. Variable selecti...
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02/28/2021
Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-observations
Survival outcomes are common in comparative effectiveness studies and re...
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12/11/2020
A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome
In the absence of a randomized experiment, a key assumption for drawing ...
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08/18/2020
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
The preponderance of large-scale healthcare databases provide abundant o...
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08/17/2020
Estimating Heterogeneous Survival Treatment Effect via Machine/Deep Learning Methods in Observational Studies
The rise of personalized medicine necessitates improved causal inference...
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01/17/2020
Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome
There is a dearth of robust methods to estimate the causal effects of mu...
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04/02/2019
Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death
Evidence supporting the current World Health Organization recommendation...
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04/02/2019
Modeling the Causal Effect of Treatment Initiation Time on Survival: Application to HIV/TB Co-infection
The timing of antiretroviral therapy (ART) initiation for HIV and tuberc...
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01/11/2019