Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. We first develop a theoretical foundation of causal inference for individualized treatment effects based on information theory. Next, we use this theory, to construct an information-optimal Bayesian causal inference algorithm.  This algorithm embeds the potential outcomes in a vector-valued reproducing kernel Hilbert space and uses a multi-task Gaussian process prior over that space to infer the individualized causal effects. We show that our algorithm significantly outperforms the state-of-the-art causal inference algorithms. The talk will conclude with a discussion of the impact of this work on precision medicine and clinical trials.

STS – Statistical scalability

• Speaker: Mihaela van der Schaar (University of Oxford)
• Thursday 15 March 2018, 11:00–12:00
• Venue: Seminar Room 2, Newton Institute.
• Series: Isaac Newton Institute Seminar Series; organiser: info@newton.ac.uk.