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About

I am a Ph.D. student in Computer Science at Columbia University, advised by Elias Bareinboim, working on generalizability in ML from a causal perspective.

I use the language of causal inference to formalize the inductive biases that enable the agents to make cross-population inferences. Through this lens, I study the theoretical limitations of learning in tasks such as domain generalization, domain adaptation, and transfer learning.

Before joining Columbia, I completed my undergraduate degree in Computer Science and Economics at Sharif University of Technology, Tehran, Iran.

Papers

Adapting, Fast and Slow: A Causal Approach to Few-Shot Sequence Learning

Kasra Jalaldoust, Elias Bareinboim

Columbia CausalAI Laboratory, 2025 May

technical report

Partial Transportability for Domain Generalization

Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim

Proceedings of the 38th Annual Conference on Neural Information Processing Systems., 2024 May

technical report

Transportable Representations for Domain Generalization

Kasra Jalaldoust, Elias Bareinboim

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, 2024 Mar, pp. 12790-12800

technical report

Pre-PhD

Multi-Domain Causal Discovery in Bijective Causal Models

Kasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash

Causal Learning and Reasoning (CLeaR), 2025

technical report

Causal Discovery in Hawkes Processes by Minimum Description Length

Kasra Jalaldoust, Kateřina Hlaváčková-Schindler, Claudia Plant

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, 2022 Jun, pp. 6978-6987

technical report