Talk
in
Workshop: NeurIPS'24 Workshop on Causal Representation Learning
Frontiers of Counterfactual Outcome Estimation in Time Series (Invited Talk by Yan Liu)
Yan Liu
Abstract:
Recent development in deep learning has spurred research advances in time series modeling and analysis. In particular, estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce. In this talk, I will discuss our recent work in counterfactual outcome estimation in time series, including an examination of balancing strategy for counterfactual estimation as well as a self-supervised learning framework.
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