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Observational-Interventional Priors for Dose-Response Learning

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. Green open access

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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
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 > 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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