Advancing Causal Structure Learning: Addressing Challenges with Latent Confounders

Music City Center 

Recently, causality has garnered significant interest within the research communities of statistics, machine learning, and computer science. A central challenge in this field is uncovering the underlying causal structures and models. Traditional methods for causal structure learning often assume the absence of latent confounders. In this talk, I will highlight recent advances in causal structure learning that specifically address the challenges posed by latent confounders. I will focus on three key techniques and their associated structural or distributional constraints, which enable us to identify latent variables, determine their cardinalities, and map out the structure involving both latent and observed variables.

Casual Discovery

Latent Confounders