I am a Ph.D. student in Computer Science at Columbia University, advised by Elias Bareinboim.

I research causal inference and its pivotal role in establishing generalizable machine learning. In particular, I utilize the formal language of causality to understand the fundamental challenges of prediction in the out-of-distribution (OOD) generalization problem. My primary focus is on the theoretical aspects of the topic, such as the asymptotic properties of learning algorithms and their performance guarantees.

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

Email: kasra at cs dot columbia dot edu

Papers

• Transportable Representations for Out-of-distribution Generalization
  K. Jalaldoust, E. Bareinboim.
  Columbia CausalAI Laboratory, Technical Report, May, 2023.

• Causal Discovery in Hawkes Processes by Minimum Description Length
  K. Jalaldoust, Katerina Schindlerova, Claudia Plant
  AAAI-2022. In Proceedings of the 36th AAAI Conference on Artificial Intelligence.
  Oral Presentation (<1%, out of 9020 papers)