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Multi-task causal learning with Gaussian processes
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Aglietti, Virginia, Damoulas, Theodoros, Alvarez, Mauricio and Gonzalez, Javier (2020) Multi-task causal learning with Gaussian processes. In: Thirty-fourth Conference on Neural Information Processing Systems, Virtual conference, 7-12 Dec 2020. Published in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 33 pp. 6293-6304.
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Official URL: https://proceedings.neurips.cc/paper/2020/hash/45c...
Abstract
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model. This is useful when we are interested in jointly learning the causal effects of interventions on different subsets of variables in a DAG, which is common in field such as healthcare or operations research. We propose the first multi-task causal Gaussian process (GP) model, which we call DAG-GP, that allows for information sharing across continuous interventions and across experiments on different variables. DAG-GP accommodates different assumptions in terms of data availability and captures the correlation between functions lying in input spaces of different dimensionality via a well-defined integral operator. We give theoretical results detailing when and how the DAG-GP model can be formulated depending on the DAG. We test both the quality of its predictions and its calibrated uncertainties. Compared to single-task models, DAG-GP achieves the best fitting performance in a variety of real and synthetic settings. In addition, it helps to select optimal interventions faster than competing approaches when used within sequential decision making frameworks, like active learning or Bayesian optimization.
Item Type: | Conference Item (Lecture) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Gaussian processes -- Data processing, Artificial intelligence, Gaussian processes, Graph theory -- Data processing | ||||||
Journal or Publication Title: | Advances in Neural Information Processing Systems 33 (NeurIPS 2020) | ||||||
Publisher: | Curran Associates | ||||||
Official Date: | 2020 | ||||||
Dates: |
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Volume: | 33 | ||||||
Page Range: | pp. 6293-6304 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 1 October 2020 | ||||||
Date of first compliant Open Access: | 20 October 2021 | ||||||
Conference Paper Type: | Lecture | ||||||
Title of Event: | Thirty-fourth Conference on Neural Information Processing Systems | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Virtual conference | ||||||
Date(s) of Event: | 7-12 Dec 2020 | ||||||
Related URLs: | |||||||
Open Access Version: |
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