About

I am a Ph.D. student in the Machine Learning Department at Carnegie Mellon University where I am advised by Zachary Lipton and David Childers. My research interests are broadly in problems at the intersection of causal inference, machine learning, and sequential decision-making. Before graduate school, I was a software engineer at Google. During my undergraduate studies, I majored in Computer Science at NTU, Singapore.

Papers

• Learned Causal Method Prediction [arxiv]
  Shantanu Gupta, Cheng Zhang, Agrin Hilmkil
  Preprint
• Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs [arxiv] [code]
  Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
  Uncertainty in Artificial Intelligence (UAI), 2024
• Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction [arxiv]
  Tom Yan, Shantanu Gupta, Zachary Lipton
  Preprint
• Local Causal Discovery for Estimating Causal Effects [arxiv] [code]
  Shantanu Gupta, David Childers, Zachary Lipton
  Causal Learning and Reasoning (CLeaR), 2023
• Efficient Online Estimation of Causal Effects by Deciding What to Observe [arxiv] [code]
  Shantanu Gupta, Zachary Lipton, David Childers
  Advances in Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)
• Correcting Exposure Bias for Link Recommendation [arxiv] [code]
  Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang
  International Conference on Machine Learning (ICML), 2021
• Estimating Treatment Effects with Observed Confounders and Mediators [arxiv]
  Shantanu Gupta, Zachary Lipton, David Childers
  Uncertainty in Artificial Intelligence (UAI), 2021