Guest talk

Francesco Locatello (Institute of Science and Technology Austria (ISTA)): Discovering and Estimating Causal Effects from Raw Experimental Data

Time: April 2, 9:30
Venue: T2, Aalto University CS building (Konemiehentie 2, Espoo)
Host: Pekka Marttinen

Abstract:
Deciphering raw, high-dimensional, and temporal observations into causal knowledge is a key component of the scientific discovery process and a longstanding challenge for AI. Across scientific disciplines, the data that can be recorded do not directly expose causal variables, which often remain latent and only indirectly measured. In this talk, I present our recent work on accurate causal effect estimation from raw experimental data and its interdisciplinary applications in ecological experiments, biology, and climate sciences. I begin by defining when a predictor constitutes a causally valid proxy of a latent variable, and how deep learning models can process entire experiments to yield correct causal conclusions. I then show how AI models enable "looking at the data first" and discovering treatment effects without supervision. Finally, I discuss how these ideas extend beyond standard causal models to 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.