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Member Retention Data Report to INCOSE

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Carnegie Mellon University Software Engineering Institute Pittsburgh United States

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Causal analysis is performed on membership data using characteristics of the data that is fed into three different algorithms, which use different methods to identify causality and are based on different assumptions. Depending on the amount of data and how strong is the causality, the methods can produce different results. Hence if different algorithms all show causality, it is highly likely that there actually is causality. For this reason four algorithms were run. They are called PC for the inventors names, FGES for fast greedy equivalent search, and FCI for fast causal inference. FGES has an option to run with different criteria for scoring graphs, based on assumptions. We used two, one called DBIC digital binary information criterion and one called BDEU Bayesian Dirichlet-likelihood equivalence and uniform.

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Technical Report



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Approved For Public Release;

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