Silva, R;
(2016)
Observational-Interventional Priors for Dose-Response Learning.
In:
Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016).
NIPS Proceedings: Barcelona, Spain.
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Abstract
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the doseresponse curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants.
Type: | Proceedings paper |
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Title: | Observational-Interventional Priors for Dose-Response Learning |
Event: | Advances in Neural Information Processing Systems 29 (NIPS 2016) |
Location: | Barcelona, Spain |
Dates: | 05 December 2016 - 10 December 2016 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://papers.nips.cc/ |
Language: | English |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1529389 |
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