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09/13/2023
Empirical Bayes Double Shrinkage for Combining Biased and Unbiased Causal Estimates
Motivated by the proliferation of observational datasets and the need to...
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09/06/2023
A Bayesian Nonparametric Method to Adjust for Unmeasured Confounding with Negative Controls
Unmeasured confounding bias is among the largest threats to the validity...
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08/03/2023
Estimating causal quantile exposure response functions via matching
We develop new matching estimators for estimating causal quantile exposu...
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08/01/2023
Causal exposure-response curve estimation with surrogate confounders: a study of air pollution and children's health in Medicaid claims data
In this paper, we undertake a case study in which interest lies in estim...
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07/05/2023
Differential recall bias in estimating treatment effects in observational studies
Observational studies are frequently used to estimate the effect of an e...
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06/05/2023
Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter
Exposure to fine particulate matter (PM_2.5) poses significant health ri...
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02/22/2023
Confounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology
Several epidemiological studies have provided evidence that long-term ex...
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02/06/2023
Causal Shift-Response Functions with Neural Networks: The Health Benefits of Lowering Air Quality Standards in the US
Policymakers are required to evaluate the health benefits of reducing th...
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05/18/2021
Measuring performance for end-of-life care
Although not without controversy, readmission is entrenched as a hospita...
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05/07/2021
Bayesian Modeling for Exposure Response Curve via Gaussian Processes: Causal Effects of Exposure to Air Pollution on Health Outcomes
Motivated by environmental health research on air pollution, we address ...
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02/21/2021
Accounting for recall bias in case-control studies: a causal inference approach
A case-control study is designed to help determine if an exposure is ass...
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02/20/2021
Assessing the causal effects of a stochastic intervention in time series data: Are heat alerts effective in preventing deaths and hospitalizations?
We introduce a new causal inference framework for time series data aimed...
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12/12/2020
From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
This paper discusses the fundamental principles of causal inference - th...
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09/18/2020
Causal Rule Ensemble: Interpretable Inference of Heterogeneous Treatment Effects
In environmental epidemiology, it is critically important to identify su...
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07/24/2020
Cross-study learning for generalist and specialist predictions
Jointly using data from multiple similar sources for the training of pre...
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09/15/2019
Causal inference and machine learning approaches for evaluation of the health impacts of large-scale air quality regulations
We develop a causal inference approach to estimate the number of adverse...
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12/17/2018
Matching on Generalized Propensity Scores with Continuous Exposures
Generalized propensity scores (GPS) are commonly used when estimating th...
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11/14/2018
A causal inference framework for cancer cluster investigations using publicly available data
Often, a community becomes alarmed when high rates of cancer are noticed...
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09/13/2018
Causal Impact of the Hospital Readmissions Reduction Program on Hospital Readmissions and Mortality
Estimating causal effects of the Hospital Readmissions Reduction Program...
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06/04/2018
A Causal Exposure Response Function with Local Adjustment for Confounding
In the last two decades, ambient levels of air pollution have declined s...
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05/29/2018
aipred: A Flexible R Package Implementing Methods for Predicting Air Pollution
Fine particulate matter (PM_2.5) is one of the criteria air pollutants r...
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05/24/2018
Estimating Population Average Causal Effects in the Presence of Non-Overlap: A Bayesian Approach
Most causal inference studies rely on the assumption of positivity, or o...
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05/13/2018
A Bayesian semiparametric framework for causal inference in high-dimensional data
We introduce a Bayesian framework for estimating causal effects of binar...
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02/19/2018
Discovering Effect Modification and Randomization Inference in Air Pollution Studies
Studies have shown that exposure to air pollution, even at low levels, s...
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01/04/2018
Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective
There are serious drawbacks to many current variable importance (VI) met...
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12/02/2017