About Me
I am a final-year PhD student at the Centre for Artificial Intelligence, University College London, working on causal inference and abstraction, supervised by Matt Kusner and Ricardo Silva. I have also worked extensively with Arthur Gretton on the intersection of Kernel Methods and Causal Inference. I am affiliated with ELLIS; my industry host is Dominik Janzing.
My research interests are three-fold: 1. in understanding the foundations of how causal structures arise from fine-grained models, 2. in causal inference under weak observability conditions such as hidden confounding and mismeasured treatments, and 3. how causality plays a role in understanding the behaviour of modern deep learning models.
From summer 2022 to spring 2023, I interned at Amazon Research Tuebingen, working on the foundations of causal abstraction, and then at Microsoft Research Cambridge, where I worked with Cheng Zhang on causal inference with latent treatments and many measurements.
Prior to starting my PhD, I earned a master’s degree in Machine Learning from University College London and my undergraduate degree in Mathematics from the University of Cambridge.
News
01.2024 New work: Meaningful Causal Aggregation and Paradoxical Confounding has been accepted to CLeaR 2024.
04.2023 I gave an invited talk on Causal Inference Under Treatment Measurement Error: A Nonparametric Instrumental Variable Approach at Causality Seminar (China). Talk slides are here.
04.2023 I gave an invited talk on confounded causal inference with proxies or measurement error at Causal Inference Meets Statistics Quarterly Meeting. Talk slides are here and the poster is here.
01.2023 I am thrilled to join Microsoft Research Cambridge to work with Cheng Zhang on causal inference with many measurements from 01.2023 to 03.2023.
06.2022 I am thrilled to join Amazon Science Tuebingen to work with Dominik Janzing on abstracting causal models from 06.2022 to 12.2022.
05.2022 New work: Causal Inference Under Treatment Measurement Error: A Nonparametric Instrumental Variable Approach has been invited for an oral presentation at UAI 2022.
05.2022 I am presenting my work on causal effect estimation with latent variables(slides) at the Statistics for Data-Centric Engineering Seminar Series, Alan Turing Institute and on structured treatment effect estimation(slides) at Professor Menggang Yu’s group, University of Wisconsin, Madison.
10.2021 New work: Causal Effect Estimation for Structured Treatments has been accepted to NeurIPS 2021.
05.2021 New work: Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictions has been accepted to ICML 2021.