Vahid Balazadeh

PhD Student in Computer Science at the University of Toronto


I’m a Ph.D. student in Computer Science at the University of Toronto, supervised by Rahul G. Krishnan. I’m interested in understanding the concepts and mechanisms that help humans in optimal decision-making, especially in healthcare. To this end, I’m working on causality and its relationship with machine learning, as well as reinforcement learning from offline observations. In particular, my research involves causal estimation and partial identification of causal effects in high-dimensional data using machine learning. I also like to explore ideas from causality, such as causal representation learning, to design interpretable machine learning methods that are robust to out-of-distribution data.

Before starting my Ph.D., I was a research intern at MPI-SWS, where I had the chance to work with Manuel Gomez Rodriguez on human-machine collaboration in reinforcement learning.

I have earned a double major B.Sc. in Computer Engineering and Mathematics from the Sharif University of Technology.


  1. Partial Identification of Treatment Effects with Implicit Generative Models
    Vahid Balazadeh, Vasilis Syrgkanis, and Rahul G. Krishnan
    Advances in Neural Information Processing Systems (NeurIPS) 2022
  2. Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes
    Vahid Balazadeh, Abir De, Adish Singla, and 1 more author
    Transactions on Machine Learning Research (TMLR) 2022


  1. Reinforcement Learning Under Algorithmic Triage
    Eleni Straitouri, Adish Singla, Vahid Balazadeh, and 1 more author
    arXiv preprint arXiv:2109.11328 2021
  2. OCDaf: Ordered Causal Discovery with Autoregressive Flows
    Hamidreza Kamkari, Vahid Zehtab, Vahid Balazadeh, and 1 more author
    arXiv preprint arXiv:2308.07480 2023