Wednesday, May 11, 2022 - 1:00pm to 2:00pm

Speaker Information

Karthika Mohan and Chi Zhang


Majority of modern machine learning algorithms assume that data are iid and missingness/incompleteness are random. However, these assumptions rarely hold in real world datasets. For instance, the wealthy are less likely to reveal their income (income causes its own missingness) and an individual’s decision to get vaccinated is influenced by his family, friends and work colleagues. In this talk we will discuss the following, (i) modeling non-iid and missing data generation processes using causal Bayesian networks, (ii) determining whether or not a quantity of interest is recoverable under missingness, (iii) identifying conditions under which bias is to be expected given non-iid data and finally (iv) algorithm for eliminating bias given non-iid data.

Speaker Bio

Karthika Mohan is an Assistant Professor of Computer Science in the School of EECS at Oregon State University. Before joining Oregon State University she was a postdoctoral scholar in the Computer Science department at University of California, Berkeley mentored by Stuart Russell. Karthika received her PhD in Computer Science (Artificial Intelligence) from University of California, Los Angeles (UCLA) where she was advised by Judea Pearl. Her research is of an interdisciplinary nature and her areas of interest include Causal Inference, Graphical Models and AI Safety. She was awarded the Google Outstanding Graduate Research Award, 2017 which is a UCLA Commencement Award. Currently she serves on the editorial board of the Journal of Causal Inference.

Chi Zhang is a graduate student advised by Judea Pearl at the Computer Science Department in University of California, Los Angeles. Her research is mainly about causality, with a focus on causal inference under interference.