Causal discovery using a Bayesian local causal discovery algorithm.
This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the "locality" for the identification of pair-wise causal relationships. BLCD takes as input a dataset and outputs potential causes of the form variable X causally influences variable Y. Identification of the causal factors of diseases and outcomes, can help formulate better management, prevention and control strategies for the improvement of health care. In this study we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 re-cords randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year--whether the infant survived or not. Using the infant birth and death dataset as input, BLCD out-put six purported causal relationships. Three out of the six relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample.
Mani
Subramani
S
Center for Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA. mani@cs.uwm.edu
Cooper
Gregory F
GF
D000465:Algorithms; D001499:Bayes Theorem; D001723:Birth Rate; D015984:Causality; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D008390:Markov Chains; D016571:Neural Networks, Computer; D014481:United States / Q000453:epidemiology