Eric and Wendy Schmidt Center Funded Research and Innovation Scholar
Causal Inference and Reinforcement Learning
- Artificial Intelligence and Machine Learning
In many applications the end goal of causal inference is not necessarily to learn the underlying causal system but to infer the best interventions in order to push the underlying system towards a desired state. This is the case for example when studying reprogramming, where the goal is to determine the best interventions (e.g. over-expression of particular transcription factors) to push a differentiated cell towards the stem cell state. In this project, the goal is to build on methods in active learning, RL and causal inference to obtain methods for selecting the best interventions in order to push the system towards a desired state.
I’m participating in this SuperUROP because I am interested in statistics and inference research. I would like to obtain more experience with inference while learning about causal inference, which is applicable to many fields including economics, the social sciences, and biology. I hope to learn relevant background on existing problems and solutions in causal inference and hopefully produce work worth publishing.