I am a ML student researching theoretical foundations and methodology in causal inference. My research interests revolve around designing efficient and flexible algorithms for causal effect identification.
I'm motivated to tackle questions related to (partial) causal effect identification. I like to use techniques from kernel methods and operator learning to solve these questions.
This website hosts my technical pages. I aim to populate the site with interesting theoretical results for the benefit of my own learning and for technical communication. Stay tuned for update!