8:00 -8:05

Opening and Welcome

8:05 – 8:35

Keynote Talk: Computational Discovery of Causes

Prof. Clark Glymour, Carnegie Mellon University

8:35 – 8:55

Predictive and Causal Implications of using Shapley Value for Model Interpretation (pdf)

Sisi Ma and Roshan Tourani

8:55 – 9:15

Continuous Treatment Effect Estimation via Generative Adversarial De-confounding (pdf)

Yunzhe Li, Kun Kuang, Bo Li, Peng Cui, Jianrong Tao, Hongxia Yang and Fei Wu

9:15 – 9:30

-------- Coffee Break --------

9:30 – 10:20

Keynote Talk: Causal Inference Under Interference And Network Uncertainty

Prof. Ilya Shpitser, Johns Hopkins University

10:20 – 11:10

Keynote Talk: On the Causal Foundations of AI

Prof. Elias Bareinboim, Columbia University

11:10 – 11:30

Hi-CI: Deep Causal Inference in High Dimensions (pdf)

Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, and Gautam Shroff

11:30 – 11:50

Learning Latent Causal Structures with a Redundant Input Neural Network (pdf)

Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, and Xinghua Lu

11:50 – 12:00

Closing