My overarching goal in research is developing methods that support informed decisions and interventions in settings that require precision and quantified uncertainty, even when available data are imperfect. This entails exploiting tools from causal inference, statistics, and machine learning. Despite the fascinating methodological advances in the field of causal inference over the past few decades, there still remain a plethora of open problems and exciting challenges in this research area. In my research, I pursue multiple directions to continue to provide solutions to open problems and bridge the gap between theory and scientific applications in healthcare, public policy, and social science.
Here is a list of my publications, divided by subject area. (* indicates equal contribution)
See my
Google Scholar
for a complete list of publications.
Causal Inference
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Semiparametric sensitivity analysis: Unmeasured confounding in observational studies
Daniel Scharfstein, Razieh Nabi, Edward Kennedy, Ming-Yueh Huang, Matteo Bonvini, and Marcela Smid
[arXiv]
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Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
Razieh Nabi*, Rohit Bhattacharya*, and Ilya Shpitser
Journal of Machine Learning Research (JMLR), 2022.
[paper]
[arXiv]
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On Testability of the Front-Door Model via Verma Constraints
Rohit Bhattacharya and Razieh Nabi
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
[paper]
[code]
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Semiparametric Causal Sufficient Dimension Reduction of Multidimensional Treatments
Razieh Nabi, Todd McNutt, and Ilya Shpitser
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
[paper]
[slides]
[code]
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Causal Inference In The Presence Of Interference In Sponsored Search Advertising
Razieh Nabi, Joel Pfeiffer, Denis Charles, and Emre Kiciman
Frontiers in big Data, 2022.
[paper]
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Estimation of Personalized Effects Associated with Causal Pathways
Razieh Nabi, Phyllis Kanki, and Ilya Shpitser
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
[paper]
[supplement]
Missing Data
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Graphical Models of Entangled Missingness
Ranjani Srinivasan, Rohit Bhattacharya, Razieh Nabi, Elizabeth L. Ogburn, and Ilya Shpitser
[arXiv]
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Causal and Counterfactual Views of Missing Data Models
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, and James Robins
[manuscript]
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On Testability and Goodness of Fit Tests in Missing Data Models
Razieh Nabi and Rohit Bhattacharya
[arXiv]
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Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Razieh Nabi*, Rohit Bhattacharya*, and Ilya Shpitser
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020
[paper]
[supplement]
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Identification in Missing Data Models Represented by Directed Acyclic Graphs
Razieh Nabi*, Rohit Bhattacharya*, Ilya Shpitser, and James Robins
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
[paper]
[supplement]
Algorithmic Fairness
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Optimal Training of Fair Predictive Models
Razieh Nabi, Daniel Malinsky, and Ilya Shpitser
Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR), PMLR 177:594-617, 2022.
[paper]
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Learning Optimal Fair Policies
Razieh Nabi, Daniel Malinsky, and Ilya Shpitser
Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
[paper]
[supplement]
[slides]
[code]
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Fair Inference on Outcomes
Razieh Nabi and Ilya Shpitser
Proceedings of the 32nd Conference on Association for the Advancement of Artificial Intelligence (AAAI), 2018.
[paper]
[slides]
[code]
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A Semiparametric Approach to Interpretable Machine Learning
Numair Sani, Jaron Lee, Razieh Nabi, and Ilya Shpitser
[arXiv]
PhD Thesis
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CAUSAL INFERENCE METHODS FOR BIAS CORRECTION IN DATA ANALYSES
[manuscript]