A theoretical study of Y structures for causal discovery
This work addresses a key bottleneck in causal inference for researchers and practitioners by enabling reliable discovery in complex scenarios with hidden variables.
The paper tackles the problem of causal discovery from observational data with unobserved common causes by introducing the first computationally feasible score-based algorithm that reliably identifies causal relationships in the large sample limit for discrete models, based on identifying Y substructures.
There are several existing algorithms that under appropriate assumptions can reliably identify a subset of the underlying causal relationships from observational data. This paper introduces the first computationally feasible score-based algorithm that can reliably identify causal relationships in the large sample limit for discrete models, while allowing for the possibility that there are unobserved common causes. In doing so, the algorithm does not ever need to assign scores to causal structures with unobserved common causes. The algorithm is based on the identification of so called Y substructures within Bayesian network structures that can be learned from observational data. An example of a Y substructure is A -> C, B -> C, C -> D. After providing background on causal discovery, the paper proves the conditions under which the algorithm is reliable in the large sample limit.