Mila Tea Talk (2022)
Some Steps Toward Causal Representation Learning
Abstract
High-dimensional unstructured data such as images or sensor data can often be collected cheaply in experiments, but is challenging to use in a causal inference pipeline without extensive engineering or labelling to extract underlying latent factors. The goal of causal representation learning is to find appropriate assumptions and methods to disentangle latent variables and learn the causal mechanisms that explain a system’s behaviour. In this talk, I’ll present results from a series of recent papers that describe how we can leverage assumptions about a system’s causal mechanisms to disentangle latent variables with identifiability guarantees. I will also talk about the importance of considering object centric learning for identifying latents, and the limitations of a commonly used injectivity assumption. Finally, I’ll discuss a hierarchy of disentanglement settings that do not require injectivity, but are important to solve if we want to build systems that can discover the underlying dynamics of complex systems from high dimensional observations.