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.

Research

• Online Data Collection for Efficient Semiparametric Inference [arxiv] [code]
  Shantanu Gupta, Zachary Lipton, David Childers
  An earlier version of this work was accepted to NeurIPS, 2021.
• Failure Modes of LLMs for Causal Reasoning on Narratives [arxiv] [code]
  Khurram Yamin*, Shantanu Gupta*, Gaurav Ghosal, Zachary Lipton, Bryan Wilder
  
• A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns [arxiv]
  Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar, Zachary Lipton, Liu Leqi
  
• Learned Causal Method Prediction [arxiv]
  Shantanu Gupta, Cheng Zhang, Agrin Hilmkil
  
• 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
  
• Local Causal Discovery for Estimating Causal Effects [arxiv] [code]
  Shantanu Gupta, David Childers, Zachary Lipton
  Causal Learning and Reasoning (CLeaR), 2023
• 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