The Recovery of Causal Poly-Trees from Statistical Data
This addresses the challenge of inferring causal networks from limited data for researchers in statistics and machine learning, though it appears incremental as it builds on existing causal recovery methods.
The paper tackles the problem of recovering causal poly-trees from statistical data by developing a method that guarantees precise recovery of topological structure and causal directionality from pairwise probability distributions, with minimal external semantics required.
Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering ply-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a ply-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.