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Poster
Dynamic Causal Bayesian Optimization
Virginia Aglietti · Neil Dhir · Javier González · Theodoros Damoulas

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @
in Poster Session 8 »

We study the problem of performing a sequence of optimal interventions in a dynamic causal system where both the target variable of interest, and the inputs, evolve over time. This problem arises in a variety of domains including healthcare, operational research and policy design. Our approach, which we call Dynamic Causal Bayesian Optimisation (DCBO), brings together ideas from decision making, causal inference and Gaussian process (GP) emulation. DCBO is useful in scenarios where the causal effects are changing over time. Indeed, at every time step, DCBO identifies a local optimal intervention by integrating both observational and past interventional data collected from the system. We give theoretical results detailing how one can transfer interventional information across time steps and define a dynamic causal GP model which can be used to find optimal interventions in practice. Finally, we demonstrate how DCBO identifies optimal interventions faster than competing approaches in multiple settings and applications.

Keywords: Optimization · Causality · Kernel Methods

Author Information

Virginia Aglietti (University of Warwick)
Neil Dhir (The Alan Turing Institute)
Javier González (Microsoft Research Cambridge)
Theodoros Damoulas (University of Warwick)

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