For a complete list of publications, see my Google Scholar Page.

## Working Papers / Under Review

**A. Ghassami**, I. Shpitser, and E. Tchetgen Tchetgen, “Partial Identification of Causal Effects Using Proxy Variables,” arXiv preprint arXiv:2304.04374. [

__link__]

E. Mokhtarian, S. Salehkaleybar,

**A. Ghassami**, and N. Kiyavash, "A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models," arXiv preprint arXiv:2205.10083. [

__link__]

**A. Ghassami**, A. Yang, D. Richardson, I. Shpitser, and E. Tchetgen Tchetgen, “Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects,” arXiv preprint arXiv:2201.10743. [

__link__]

**A. Ghassami**, A. Yang, I. Shpitser, and E. Tchetgen Tchetgen, “Causal Inference with Hidden Mediators,” arXiv preprint arXiv:2111.02927. [

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**A. Ghassami**, and I. Shpitser, “Partially Intervenable Causal Models,” arXiv preprint arXiv:2110.12541v1. [

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**A. Ghassami**, N. Sani, Y. Xu, and I. Shpitser, “Multiply Robust Causal Mediation Analysis with Continuous Treatments,” arXiv preprint arXiv:2105.09254. [

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**A. Ghassami**, S. Salehkaleybar, and N. Kiyavash, “Interventional Experiment Design for Causal Structure Learning,” under submission. arXiv preprint arXiv:1910.05651. [

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## Selected Peer-Reviewed Research Publications

Y. Yang,

Y. Yang, M. Nafea,

S. Akbari, E. Mokhtarian,

S. Khodadadian,

E. Mokhtarian, S. Akbari,

I. Ng,

A. Yang,

S. Salehkaleybar,

R. Tahir, T. Khan, X. Gong,

C.Chen,

**A. Ghassami**, M. Nafea, N. Kiyavash, K. Zhang, and I. Shpitser, "Causal Discovery in Linear Latent Variable Models Subject to Measurement Error," Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2022.****

A. Ghassami, A. Ying, I. Shpitser, and E Tchetgen Tchetgen, “Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference,” Proceedings of the International Conference on Artificial Intelligence and StatisticsA. Ghassami

**(AISTATS)**, 2022. [__link__]Y. Yang, M. Nafea,

**A. Ghassami**, and N. Kiyavash, “Causal Discovery in Linear Structural Causal Models with Deterministic Relations,” Proceedings of the conference on Causal Learning and Reasoning**(CLeaR)**, 2022. [__link__]S. Akbari, E. Mokhtarian,

**A. Ghassami**, and N. Kiyavash, “Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias,” Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2021. [__link__]S. Khodadadian,

**A. Ghassami**, and N. Kiyavash, “Impact of Data Processing on Fairness in Supervised Learning,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2021. [__link__]E. Mokhtarian, S. Akbari,

**A. Ghassami**, and N. Kiyavash, “A Recursive Markov Boundary-Based Approach to Causal Structure Learning,” In The KDD'21 Workshop on Causal Discovery, 2021. [__link__]I. Ng,

**A. Ghassami**, and K. Zhang, “On the Role of Sparsity and DAG Constraints for Learning Linear DAGs,” Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2020. [__link__]A. Yang,

**A. Ghassami**, M. Raginsky, N. Kiyavash, and E. Rosenbaum, “Model-Augmented Conditional Mutual Information Estimation for Feature Selection," Proceedings of the conference on Uncertainty in Artificial Intelligence**(UAI)**, 2020. [__link__]**A. Ghassami**, A.Yang, N. Kiyavash, and K. Zhang, “Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs,” Proceedings of the International Conference on Machine Learning**(ICML)**, 2020. [__link__]S. Salehkaleybar,

**A. Ghassami**, N. Kiyavash, and K. Zhang, “Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables,” Journal of Machine Learning Research**(JMLR)**, 2020. [__link__]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and K. Zhang, “Counting and Sampling from Markov Equivalent DAGs Using Clique Trees,” Proceedings of the Association for the Advancement of Artificial Intelligence**(AAAI)**, 2019. [__link__]**A. Ghassami**, N. Kiyavash, B. Huang, and K. Zhang, “Multi-Domain Causal Structure Learning in Linear Systems,” Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2018. [link]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and E. Bareinboim, “Budgeted Experiment Design for Causal Structure Learning,” Proceedings of the International Conference on Machine Learning**(ICML)**, 2018. [link]**A. Ghassami**, S. Khodadadian, and N. Kiyavash, “Fairness in Supervised Learning: An Information Theoretic Approach,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2018. [link]**A. Ghassami**, and N. Kiyavash, “A Covert Queueing Channel in FCFS Schedulers,” IEEE Transactions on Information Forensics and Security, 13(6), pp.1551-1563, 2018. [link]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and K. Zhang. “Learning Causal Structures Using Regression Invariance,” Proceedings of the Advances in Neural Information Processing Systems**(NIPS)**, 2017. [link]**A. Ghassami**, and N. Kiyavash, “Interaction Information for Causal Inference: The Case of Directed Triangle,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2017.**A. Ghassami**, D. Cullina, and N. Kiyavash, “Message Partitioning and Limited Auxiliary Randomness: Alternatives to Honey Encryption,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2016.R. Tahir, T. Khan, X. Gong,

**A. Ghassami**, M. Caesar, and N. Kiyavash, “Sneak-Peek: High Speed Covert Channels in Data Center Networks,” Proceedings of the International Conf. on Computer Communications**(INFOCOM)**, 2016.C.Chen,

**A. Ghassami**, S. Mohan, N. Kiyavash, R. Bobba, and R. Pellizzoni, “Schedule-Based Side Channel Attack in Fixed Priority Real-Time Systems,” Proceedings of the IEEE Workshop on Security and Dependability of Critical Embedded Real-Time Systems, 2016.**A. Ghassami**, X. Gong, and N. Kiyavash, “Capacity Limit of Queueing Timing Channel in Shared FCFS Schedulers,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2015.