Summary
My preliminary project title is "Meta Learning for Causality".
Meta learning is also known as 'learning to learn'. The overarching goal of my project is to create a model that is capable of adapting quickly to new settings, specifically for the purpose of causal structure learning and causal inference. Adapting quickly is a hallmark of what differentiates how humans learn from how traditional machine learning models learn a task. To this end we are first working to create new models for improved causal structure learning and causal inference tasks. My primary application of interest is to the study of causes of disease, with the goal of one day identifying interventions that can be made to either treat or prevent disease.
