Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms
Duration: 1 hour 3 mins
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About this item
| Description: |
Van der Schaar, M
Thursday 15th March 2018 - 11:00 to 12:00 |
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| Created: | 2018-03-16 13:46 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Van der Schaar, M |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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. |
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