Ehsan Mokhtarian

Orcid: 0009-0003-4761-9249

According to our database1, Ehsan Mokhtarian authored at least 16 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2020
2021
2022
2023
2024
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1
2
3
4
5
6
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2
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4
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2

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
Recursive Causal Discovery.
CoRR, 2024

QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Causal Effect Identification in a Sub-Population with Latent Variables.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

CausalCite: A Causal Formulation of Paper Citations.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

s-ID: Causal Effect Identification in a Sub-population.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models.
J. Mach. Learn. Res., 2023

CausalCite: A Causal Formulation of Paper Citations.
CoRR, 2023

Causal Effect Identification in Uncertain Causal Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect.
CoRR, 2022

Revisiting the general identifiability problem.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Causal Effect Identification with Context-specific Independence Relations of Control Variables.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Learning Bayesian Networks in the Presence of Structural Side Information.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Recursive Markov Boundary-Based Approach to Causal Structure Learning.
Proceedings of the KDD 2021 Workshop on Causal Discovery, 2021

2020
A Recursive Markov Blanket-Based Approach to Causal Structure Learning.
CoRR, 2020


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