Nick Pawlowski is a senior researcher in the Machine Intelligence group at Microsoft Research Cambridge applying deep learning techniques to solve causal questions. Before joining Microsoft Research in 2021, he pursued his PhD at the Biomedical Image Analysis Group at Imperial College London, where he worked on the combination of probabilistic inference, causality and deep learning.
Talk Title: Deep End-to-End Causal Inference
Abstract:
Causal inference is essential for data-driven decision-making as well as imagining counterfactual outcomes. Building a framework that can answer real-world causal questions at scale is critical. However, research on deep learning, causal discovery, and inference has evolved separately. In this talk, I will give an introduction into causal discovery and present a Deep End-to-end Causal Inference (DECI) framework, a single deep generative model that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect estimation (CATE).
RPL Summer School 2026 