Partial identification of principal causal effects under violations of principal ignorability
Minxuan Wu and Joseph Antonelli. ArXiv, 2024.
Selected publications
Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
Suyeon Kang, Alexander Franks, Michelle Audirac, Danielle Braun, and Joseph Antonelli. ArXiv, 2024.
Smooth and shape-constrained quantile distributed lag models
Yisen Jin, Aaron Molstad, Ander Wilson, and Joseph Antonelli. Biometrics. Revision invited, 2024.
Autoregressive models for panel data causal inference with application to state-level opioid policies
Joseph Antonelli, Max Rubinstein, Denis Agniel, Rosanna Smart, Elizabeth Stuart, Matthew Cefalu, Terry Schell, Joshua Eagan, Elizabeth Stone, Max Griswold, Mark Sorbero, and Beth Ann Griffin. ArXiv, 2024.
Treatment Effect Heterogeneity and Importance Measures for Multivariate Continuous Treatments
Heejun Shin, Antonio Linero, Michelle Audirac, Kezia Irene, Danielle Braun, Joseph Antonelli. The Annals of Applied Statistics. Revision invited, 2024.
A spatial interference approach to account for mobility in air pollution studies with multivariate continuous treatments
Heejun Shin, Danielle Braun, Kezia Irene, Michelle Audirac, and Joseph Antonelli. Journal of the American Statistical Association, Revision invited, 2023.
Principal stratification with continuous treatments and continuous post-treatment variables
Joseph Antonelli, Fabrizia Mealli, Brenden Beck, and Alessandra Mattei. ArXiv, 2023.
Robust inference for geographic regression discontinuity designs: assessing the impact of police precincts
Emmett Kendall, Brenden Beck, and Joseph Antonelli. Journal of the Royal Statistical Society: Series A. Revision invited, 2023.
Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy
Joseph Antonelli and Brenden Beck. Journal of the Royal Statistical Society: Series A, 2023.
Improved inference for doubly robust estimators of heterogeneous treatment effects
Heejun Shin and Joseph Antonelli. Biometrics, 2022.
The How and Why of Bayesian Nonparametric Causal Inference
Antonio Linero and Joseph Antonelli. WIREs Computational Statistics, 2022.
Multiple exposure distributed lag models with variable selection
Joseph Antonelli, Ander Wilson, and Brent Coull. Biostatistics, 2021.
Estimation and false discovery control for the analysis of environmental mixtures
Srijata Samanta and Joseph Antonelli. Biostatistics, 2021.
Causal Inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties
Joseph Antonelli, Georgia Papadogeorgou, and Francesca Dominici. Biometrics, 2020.
Spike-and-slab group lassos for grouped regression and sparse generalized additive models
Ray Bai*, Gemma Moran*, Joseph Antonelli*, Yong Chen, and Mary Boland. Journal of the American Statistical Association, 2020.
Estimating the health effects of environmental mixtures using Bayesian semiparametric regression and sparsity inducing priors models
Joseph Antonelli, Maitreyi Mazumdar, David Bellinger, David Christiani, Robert Wright, and Brent Coull. The Annals of Applied Statistics, 2020.
Averaging causal estimators in high dimensions
Joseph Antonelli and Matthew Cefalu. Journal of Causal Inference, 2020.
High-dimensional confounding adjustment using continuous spike and slab priors
Joseph Antonelli, Giovanni Parmigiani, and Francesca Dominici. Bayesian Analysis, 2018.
Doubly robust matching estimators for high dimensional confounding adjustment.
Joseph Antonelli, Matthew Cefalu, Nathan Palmer, and Denis Agniel. Biometrics, 2018.
Estimating the causal effect of low levels of fine particulate matter on hospitalization.
Maggie Makar*, Joseph Antonelli*, Qian Di, David Cutler, Joel Schwartz, and Francesca Dominici. Epidemiology, 2017.
A synthetic estimator for the efficacy of clinical trials with all or nothing compliance
Joseph Antonelli, Bing Han, and Matthew Cefalu. Statistics in Medicine, 2017.
Spatial multiresolution analysis of the effect of PM2.5 on birth weights
Joseph Antonelli, Joel Schwartz, Itai Kloog, and Brent Coull. Annals of Applied Statistics, 2017.
Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research.
Joseph Antonelli, Corwin Zigler, and Francesca Dominici. Biostatistics, 2017.
The positive effects of population-based preferential sampling in environmental epidemiology.
Joseph Antonelli, Matthew Cefalu, and Luke Bornn. Biostatistics, 2016.
Mitigating bias in generalized linear mixed models: The case for Bayesian nonparametrics.
Joseph Antonelli, Lorenzo Trippa, and Sebastien Haneuse. Statistical Science, 2016.
* indicates equal author contribution