Title
Causal inference in high-dimensional exploratory experiments
Abstract
Deciphering observations into structural knowledge of the world is a key component of the scientific discovery process and a longstanding challenge for AI. In this talk, I will present our recent works positioning causal representations as the bridge between perception and causal knowledge. I will begin with the challenge of prediction powered causal inference and how to train predictors that are causally valid proxies of latent variables. Next, I will also show how AI models enable "looking at the data first" and discovering effects in randomized trials without supervision. Finally, I will showcase how these ideas go beyond traditional causal models and bridge to the discovery of structure in dynamical systems.
Bio
Francesco Locatello is a tenure-track assistant professor at the Institute of Science and Technology Austria (ISTA) and an AI resident at the Chan Zuckerberg Initiative. Before, he was a senior applied scientist at Amazon Web Services, leading the Causal Representation Learning team. He received his PhD from ETH Zürich co-advised by Gunnar Rätsch and Bernhard Schölkopf. His research received several awards, including the ICML 2019 Best Paper award, the Hector Foundation award for outstanding achievements in machine learning from the Heidelberg Academy of Science in 2023, and the Google Research Scholar Award in 2024.
Practical
This seminar is organised by the RT 6 Causal Learning at Integreat.
The Tuesday seminar series explores a wide range of topics relevant to our research themes. Each week, invited researchers present for 40 minutes, followed by a group discussion.
Seminars are open for attendance for everybody.
For those unable to attend in person: https://uio.zoom.us/j/62535744013
Point of contacts:
- Organiser and moderator: Associate Professor Johan Pensar, University of Oslo
- Integreat Administrative Leader Maria Dikova, University of Oslo