Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
This work addresses inefficiencies in optimization for domains with causal side-information, offering an incremental improvement over existing methods.
The paper tackles the problem of optimizing expensive grey-box functions with known causal structure, showing that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem with efficient solutions. Experimental results demonstrate that this formulation enhances existing causal Bayesian optimization algorithms on real and synthetic benchmarks.
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.