Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure

by   Kim Hammar, et al.

We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks.


Bayesian Optimisation for Constrained Problems

Many real-world optimisation problems such as hyperparameter tuning in m...

Causal Bayesian Optimization

This paper studies the problem of globally optimizing a variable of inte...

Bayesian functional optimisation with shape prior

Real world experiments are expensive, and thus it is important to reach ...

Differentiable Multi-Target Causal Bayesian Experimental Design

We introduce a gradient-based approach for the problem of Bayesian optim...

EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions

Surrogate algorithms such as Bayesian optimisation are especially design...

Ordinal Bayesian Optimisation

Bayesian optimisation is a powerful tool to solve expensive black-box pr...

Please sign up or login with your details

Forgot password? Click here to reset