Liyuan Xu (Gatsby Computational Neuroscience Unit)
Liyuan Xu is a Ph.D. student at Gatsby Computational Neuroscience Unit and supervised by Prof. Arthur Gretton. He is interested in machine learning problems related to decision-making, specifically, multi-armed bandit, causal inference, and reinforcement learning. Liyuan was a part-time assistance support worker at RIKEN AIP and received his Bachelor's and Master’s degrees from the University of Tokyo.
Short Abstract: In order to build a trust-worthy machine learning model, it is essential to model the causal relationships between the action, or treatments, on the outcome. This is challenging when there exist unobserved confounders, which affect both treatments and the outcome and cause bias in the estimation. In this talk, we will introduce two causal methods to deal with this; One is instrumental variable regression, which uses the variable that affects the outcome only through the treatment. The other is proxy causal learning, which uses proxies, the structured side information for the confounders. We propose to learn neural network features in these methods by alternatively solving the regression problems, which avoids the need for sampling or density estimation. We also point out that these methods can be applied to the problems in reinforcement learning, such as offline-policy evaluations and confounded bandit problems.