Causality

In much of our work, we incorporate causal thinking into high-dimensional, messy machine learning problem formulations, with the goal of developing practical machine learning algorithms that can guide decisions and handling distribution shift.

• Local Causal Discovery for Estimating Causal Effects

  Shantanu Gupta, David Childers, Zachary Lipton
  Causal Learning and Reasoning (CLeaR)

• Efficient Online Estimation of Causal Effects by Deciding What to Observe

  Shantanu Gupta, Zachary Lipton, David Childers
  Advances in Neural Information Processing Systems (NeurIPS)

• Off-Policy Risk Assessment in Contextual Bandits

  Audrey Huang, Liu Leqi, Zachary Lipton, Kamyar Azizzadenesheli
  Advances in Neural Information Processing Systems (NeurIPS)

• Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data

  Cheng Cheng*, Helen Zhou*, Jeremy Weiss, Zachary Lipton
  American Medical Informatics Association (AMIA) Annual Symposium

• Estimating Treatment Effects with Observed Confounders and Mediators

  Shantanu Gupta, Zachary Lipton, David Childers
  Uncertainty in Artificial Intelligence (UAI)

• A Unified View of Label Shift Estimation

  Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary Lipton
  Advances in Neural Information Processing Systems (NeurIPS)

• Algorithmic Fairness from a Non-Ideal Perspective

  Sina Fazelpour, Zachary Lipton
  AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES)

• Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

  Divyansh Kaushik, Zachary Lipton
  International Conference on Learning Representations (ICLR)

• Causal Inference With Selectively-Deconfounded Data

  Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur
  NeurIPS Causality Workshop

• Detecting and Correcting for Label Shift with Black Box Predictors

  Zachary Lipton*, Yu-Xiang Wang*, Alex Smola
  International Conference on Machine Learning (ICML)