Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions

Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our proposed solution represents the treatment response curves using linear time-invariant dynamical systems-this provides a flexible means for modeling response over time to highly variable dose curves. Moreover, for multivariate data, the proposed method: uncovers shared structure in treatment response and the baseline across multiple markers; and, flexibly models challenging correlation structure both across and within signals over time. For this, we build upon the framework of multiple-output Gaussian Processes. On simulated and a challenging clinical dataset, we show significant gains in accuracy over stateof- the-art models.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017
PublisherAUAI Press Corvallis
StatePublished - 2017
Event33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia
Duration: Aug 11 2017Aug 15 2017

Other

Other33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017
CountryAustralia
CitySydney
Period8/11/178/15/17

ASJC Scopus subject areas

  • Artificial Intelligence

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