Restricted Causal Inference Algorithm
This work addresses a specific challenge in causal inference for researchers dealing with hidden variables, but it appears incremental as it builds directly on existing methods.
The paper tackles the problem of recovering belief network structure from data with hidden variables by extending the CI algorithm with a restriction on conditional dependency checks and additional transformation steps, and demonstrates the algorithm's correctness.
This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief network. Its correctness is demonstrated.