AAAI-20 Tutorial

Representation Learning for Causal Inference

Hilton New York Midtown, New York, USA

Date & Time: 2:00 pm - 6:00 pm, February 8, 2020

Location: Sutton South

Causal inference has numerous real-world applications in many domains such as health care, marketing, political science and online advertising. Treatment effect estimation, as a fundamental problem in causal inference, has been extensively studied in statistics for decades. However, traditional treatment effect estimation methods may not well handle large-scale and high-dimensional heterogeneous data. In recent years, an emerging research direction has attracted increasing attention in the broad artificial intelligence field, which combines the advantages of traditional treatment effect estimation approaches (e.g., matching estimators) and advanced representation learning approaches (e.g., deep neural networks). In this tutorial, we will introduce both traditional and state-of-the-art representation learning algorithms for treatment effect estimation. Background about causal inference, counterfactuals and matching estimators will be covered as well. We will also showcase promising applications of these methods in different application domains.

Tutorial Slides
Causal Inference Tutorial Slides

Survey Paper
A Survey on Causal Inference

Date: February 8, 2020
Location: Sutton South
2:00PM--2:05PM Welcome from Organizers
2:05PM--2:20PM Background on Causal Inference
2:20PM--2:50PM Classical Causal Inference Methods
2:50PM--3:20PM Subspace Learning for Causal Inference
3:20PM--3:45PM Deep Representation Learning for Causal Inference (I)
3:45PM--4:15PM Coffee Break
4:15PM--4:45PM Deep Representation Learning for Causal Inference (II)
4:45PM--5:15PM Applications
5:15PM--5:45PM Conclusions and Future Perspectives
5:45PM--6:00PM Closing Remarks


Sheng Li

Assistant Professor
University of Georgia


Liuyi Yao

PhD Candidate
University at Buffalo


Yaliang Li

Research Scientist
Alibaba Group


Jing Gao

Associate Professor
University at Buffalo


Aidong Zhang

University of Virginia