\dm_csml_event_details UCL ELLIS

Stochastic Causal Programming for Bounding Treatment Effects


Ricardo Silva


University College London


Friday, 20 January 2023




Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH



Event series

DeepMind/ELLIS CSML Seminar Series


Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effects of multivariate, continuous treatments when unmeasured confounding makes identification impossible. Specifically, we cast causal effects as objective functions within a constrained optimization problem, and minimize/maximize these functions to obtain bounds. We combine flexible learning algorithms with Monte Carlo methods to implement a family of solutions under the name of stochastic causal programming. In particular, we show how the generic framework can be efficiently formulated in settings where auxiliary variables are clustered into pre-treatment and post-treatment sets, where no fine-grained causal graph can be formulated. Contrasted to other generic approaches, this highly simplifies the problem and has advantages concerning how to encode structural knowledge without explicitly constructing latent hidden common causes. Joint work with Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner and Niki Kilbertus.


Ricardo Silva is a Professor of Statistical Machine Learning and Data Science at the Department of Statistical Science, UCL, a Faculty Fellow at the Alan Turing Institute, and a recipient of a EPSRC Open Fellowship (2023-2027). Ricardo obtained a PhD in Machine Learning from Carnegie Mellon University, 2005, followed by postdoctoral positions at the Gatsby Unit and at the Statistical Laboratory, University of Cambridge. His main interests are on causal inference, latent variable models, and probabilistic machine learning. His research has received funding from organisations such as EPSRC, Innovate UK, the Office of Naval Research, Winton Research and Adobe Research. Ricardo has also served in the senior program committee of several machine learning conferences, including the role of Senior Area Chair at NeurIPS and ICML, and Program Chair for the Uncertainty in Artificial Intelligence conference.