From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Design
This work addresses the challenge of designing cost-effective interventions for causal discovery, which is important for researchers and practitioners in fields like medicine or economics, though it appears incremental as it builds on existing causal DAG methods.
The paper tackles the problem of efficiently discovering causal relationships from observational and experimental data under practical constraints, proposing an adaptive integer programming approach that achieves full causal graph recovery with fewer intervention iterations and variable manipulations than random baselines.
Using both observational and experimental data, a causal discovery process can identify the causal relationships between variables. A unique adaptive intervention design paradigm is presented in this work, where causal directed acyclic graphs (DAGs) are for effectively recovered with practical budgetary considerations. In order to choose treatments that optimize information gain under these considerations, an iterative integer programming (IP) approach is proposed, which drastically reduces the number of experiments required. Simulations over a broad range of graph sizes and edge densities are used to assess the effectiveness of the suggested approach. Results show that the proposed adaptive IP approach achieves full causal graph recovery with fewer intervention iterations and variable manipulations than random intervention baselines, and it is also flexible enough to accommodate a variety of practical constraints.