University of Cambridge > Talks.cam > Machine Learning @ CUED > Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

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

Download to your calendar using vCal

If you have a question about this talk, please contact Adrian Weller .

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 model significantly outperforms the state-of-the-art causal inference models. The talk will conclude with a discussion of the impact of this work on precision medicine and clinical trials.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Virtual event Webinar & LT1, Computer Laboratory, William Gates Building. Cambridge Global Health Year

Other talks

Group Work Cooperation in Nature Cambridge RNA Club - IN PERSON title abstract tba Christopher Cowie on the epistemology of assessing biosignature claims An AI-Assisted Proof in Spectral Geometry
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity