Razieh Nabi

According to our database, Razieh Nabi authored at least 20 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Ananke: A Python Package For Causal Inference Using Graphical Models.

Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser

CoRR, 2023

On Testability and Goodness of Fit Tests in Missing Data Models.

Razieh Nabi, Rohit Bhattacharya

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms.

Anna Guo, Jiwei Zhao, Razieh Nabi

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis.

Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022

Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables.

Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser

J. Mach. Learn. Res., 2022

Causal Inference in the Presence of Interference in Sponsored Search Advertising.

Razieh Nabi, Joel Pfeiffer, Denis Charles, Emre Kiciman

Frontiers Big Data, 2022

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks.

Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

CoRR, 2022

Causal and counterfactual views of missing data models.

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins

CoRR, 2022

Semiparametric causal sufficient dimension reduction of multidimensional treatments.

Razieh Nabi, Todd McNutt, Ilya Shpitser

Proceedings of the Uncertainty in Artificial Intelligence, 2022

On testability of the front-door model via Verma constraints.

Rohit Bhattacharya, Razieh Nabi

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Optimal Training of Fair Predictive Models.

Razieh Nabi, Daniel Malinsky, Ilya Shpitser

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract).

Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

Proceedings of the IEEE International Conference on Big Data, 2022

2020

Causal Inference in the Presence of Interference in Sponsored Search Advertising.

Razieh Nabi, Joel Pfeiffer, Murat Ali Bayir, Denis Charles, Emre Kiciman

CoRR, 2020

A Semiparametric Approach to Interpretable Machine Learning.

Numair Sani, Jaron J. R. Lee, Razieh Nabi, Ilya Shpitser

CoRR, 2020

Full Law Identification in Graphical Models of Missing Data: Completeness Results.

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Identification In Missing Data Models Represented By Directed Acyclic Graphs.

Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Learning Optimal Fair Policies.

Razieh Nabi, Daniel Malinsky, Ilya Shpitser

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Estimation of Personalized Effects Associated With Causal Pathways.

Razieh Nabi, Phyllis Kanki, Ilya Shpitser

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Fair Inference on Outcomes.

Razieh Nabi, Ilya Shpitser

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criteria.

Razieh Nabi, Xiaogang Su

R J., 2017