Dynamic Causal Bayesian Optimization

TL;DR

This paper introduces Dynamic Causal Bayesian Optimization (DCBO), a method for sequentially identifying optimal interventions in evolving causal systems using Gaussian processes and causal inference, with proven efficiency and adaptability.

Contribution

The paper develops a novel dynamic causal Gaussian process model and theoretical framework for transferring interventional information over time, enhancing intervention optimization in changing systems.

Findings

DCBO outperforms existing methods in speed of identifying optimal interventions.

Theoretical results enable effective transfer of interventional data across time.

Demonstrated success in multiple real-world applications.

Abstract

This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from sequential decision making, causal inference and Gaussian process (GP) emulation. DCBO is useful in scenarios where all causal effects in a graph are changing over time. 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 quantify uncertainty and find optimal interventions in practice. We demonstrate how DCBO identifies optimal interventions faster than competing approaches in multiple settings and applications.