Causal Entropy Optimization
This addresses the challenge of causal effect optimization under structural uncertainty for applications in biology, operations research, and healthcare, representing a novel method rather than an incremental improvement.
The authors tackled the problem of globally optimizing causal effects in an unknown causal graph by proposing Causal Entropy Optimization (CEO), which generalizes Causal Bayesian Optimization to handle uncertainty in the graph structure. The result showed that CEO achieves faster convergence to the global optimum compared to CBO and improves upon sequential approaches in synthetic and real-world models.
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare. We propose Causal Entropy Optimization (CEO), a framework that generalizes Causal Bayesian Optimization (CBO) to account for all sources of uncertainty, including the one arising from the causal graph structure. CEO incorporates the causal structure uncertainty both in the surrogate models for the causal effects and in the mechanism used to select interventions via an information-theoretic acquisition function. The resulting algorithm automatically trades-off structure learning and causal effect optimization, while naturally accounting for observation noise. For various synthetic and real-world structural causal models, CEO achieves faster convergence to the global optimum compared with CBO while also learning the graph. Furthermore, our joint approach to structure learning and causal optimization improves upon sequential, structure-learning-first approaches.