Fair inference on outcomes R Nabi, I Shpitser Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 404 | 2018 |
Learning Optimal Fair Policies R Nabi, D Malinsky, I Shpitser Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 112 | 2019 |
Semiparametric inference for causal effects in graphical models with hidden variables R Bhattacharya, R Nabi, I Shpitser Journal of Machine Learning Research 23 (295), 1-76, 2022 | 65 | 2022 |
Full Law Identification In Graphical Models Of Missing Data: Completeness Results R Nabi, R Bhattacharya, I Shpitser Proceedings of the 37th International Conference on Machine Learning (ICML), 2020 | 52 | 2020 |
Identification In Missing Data Models Represented By Directed Acyclic Graphs R Bhattacharya, R Nabi, I Shpitser, JM Robins Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence …, 2019 | 40 | 2019 |
Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies DO Scharfstein, R Nabi, EH Kennedy, MY Huang, M Bonvini, M Smid arXiv preprint arXiv:2104.08300, 2021 | 26 | 2021 |
Semiparametric Causal Sufficient Dimension Reduction of Multidimensional Treatments R Nabi, T McNutt, I Shpitser The 38th Conference on Uncertainty in Artificial Intelligence, 2022 | 24* | 2022 |
Learning task-aware effective brain connectivity for fmri analysis with graph neural networks Y Yu, X Kan, H Cui, R Xu, Y Zheng, X Song, Y Zhu, K Zhang, R Nabi, ... 2022 IEEE International Conference on Big Data (Big Data), 4995-4996, 2022 | 19* | 2022 |
Optimal Training of Fair Predictive Models R Nabi, D Malinsky, I Shpitser Proceedings of the First Conference on Causal Learning and Reasoning 177 …, 2022 | 19 | 2022 |
Causal inference in the presence of interference in sponsored search advertising R Nabi, J Pfeiffer, D Charles, E Kıcıman Frontiers in big Data 5, 2022 | 17 | 2022 |
Estimation of Personalized Effects Associated With Causal Pathways R Nabi, P Kanki, I Shpitser Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence …, 2018 | 13 | 2018 |
On Testability of the Front-Door Model via Verma Constraints R Bhattacharya, R Nabi The 38th Conference on Uncertainty in Artificial Intelligence, 2022 | 10 | 2022 |
Causal and counterfactual views of missing data models R Nabi, R Bhattacharya, I Shpitser, J Robins arXiv preprint arXiv:2210.05558, 2022 | 8 | 2022 |
coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models R Nabi, X Su The R Journal 9 (1), 229-238, 2017 | 7 | 2017 |
Conversion rate prediction in search engine marketing R Nabi-Abdolyousefi Istanbul Sehir University, 2015 | 7 | 2015 |
On testability and goodness of fit tests in missing data models R Nabi, R Bhattacharya Uncertainty in Artificial Intelligence, 1467-1477, 2023 | 5 | 2023 |
Ananke: A Python Package For Causal Inference Using Graphical Models JJR Lee, R Bhattacharya, R Nabi, I Shpitser arXiv preprint arXiv:2301.11477, 2023 | 5 | 2023 |
A Semiparametric Approach to Interpretable Machine Learning N Sani, J Lee, R Nabi, I Shpitser arXiv preprint arXiv:2006.04732, 2020 | 5 | 2020 |
3D offline path planning for a surveillance aerial vehicle using B-splines R Nabi-Abdolyousefi, A Banazadeh Proceedings of the 2013 International Conference on Advanced Mechatronic …, 2013 | 5 | 2013 |
Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional A Guo, D Benkeser, R Nabi arXiv preprint arXiv:2312.10234, 2023 | 2 | 2023 |