Causal Discovery by Interventions via Integer Programming
This addresses limitations in causal discovery for scientific fields by providing exact solutions, but it appears incremental as it builds on optimization methods.
The paper tackled the problem of causal discovery by designing minimal intervention sets to ensure identifiability, using an integer programming approach, and demonstrated effectiveness through comparative analysis.
Causal discovery is essential across various scientific fields to uncover causal structures within data. Traditional methods relying on observational data have limitations due to confounding variables. This paper presents an optimization-based approach using integer programming (IP) to design minimal intervention sets that ensure causal structure identifiability. Our method provides exact and modular solutions that can be adjusted to different experimental settings and constraints. We demonstrate its effectiveness through comparative analysis across different settings, demonstrating its applicability and robustness.