The 2023 ACM SIGKDD Workshop on Causal Discovery,
Prediction and Decision

                                                                                                                     

August 07, 2023, Long Beach, CA

Keynote speech 1

Title: Latent Causal Graph Identification

Presenter: Prof. Biwei Huang, University of California San Diego (UCSD)

Abstract: In many cases, the prevalent assumption made by causal discovery algorithms that there are no latent confounders may not hold. In this talk, I will focus on recent advancements in causal structure learning that specifically address the presence of latent confounders. This will include locating latent variables, determining their cardinalities, identifying the structure among latent variables, and determining their relationships with measured variables. I will be discussing three primary techniques for identifying latent causal graphs, which correspond to three different scenarios: (i) rank-deficiency tests for the linear-Gaussian case, (ii) generalized independent noise condition for the linear non-Gaussian case, and (iii) representation learning for the general nonlinear case.

Biography: Biwei Huang is an assistant professor at the University of California San Diego. She received her PhD degree from Carnegie Mellon University, under the supervision of Prof. Kun Zhang and Prof. Clark Glymour. Her research interests are mainly in three aspects: (1) automated causal discovery in complex environments with theoretical guarantees, (2) advancing machine learning from the causal perspective, and (3) using or adapting causal discovery approaches to solve scientific problems. On the causality side, Huang's research has delivered more reliable and practical causal discovery algorithms by formulating and addressing the property of distribution shifts and allowing nonlinear relationships, general data distributions, latent confounders, etc. On the machine learning side, her work has shown that the causal view provides a clear picture for understanding advanced learning problems and allows going beyond the data in a principled, interpretable manner.

Keynote speech 2

Title: Achieving causal fairness in bandit based recommendation

Presenter: Prof. Xintao Wu, University of Arkansas

Abstract: In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. It is important to develop recommendation algorithms to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. We study how to leverage offline data, incorporate causal inference, and adopt soft intervention to model the arm selection strategy in contextual bandits. We present the d-separation based UCB algorithm (D-UCB) which can reduce the amount of exploration needed to achieve low cumulative regret, and the fair causal bandit (F-UCB) for achieving the counterfactual individual fairness. As the offline data often contain confounding and selection biases, ignoring these biases in causal bandits could negatively affect the performance of online recommendation. We present approaches of estimating conditional causal effects and deriving their bounds in the presence of compound biases. We further study how the derived causal bounds affect regret analysis in contextual bandits.

Biography: Dr. Xintao Wu is a professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at the University of Arkansas. He was a faculty member in College of Computing and Informatics at the University of North Carolina at Charlotte from 2001 to 2014. He got his BS degree in Information Science from the University of Science and Technology of China in 1994, ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and Ph.D. in Information Technology from George Mason University in 2001. Dr. Wu's major research interests include data mining, privacy and security, fair machine learning, and recently causal bandits and causal representation learning. Dr. Wu has published over 150 scholarly papers and received several paper awards including PAKDD'13 Best Application Paper Award, BIBM'13 Best Paper Award, CNS'19 Best Paper Award, and PAKDD'19 Most Influential Paper Award. He has also served on editorial boards of several international journals and many conference program committees of data mining and AI.