Bayesian statistics causality approaches

In a study of 1230 patients with initially unexplained cardiomyopathies, lithium was implicated in one case (114). Using a data-based mining Bayesian statistical approach to the WHO database of adverse reactions to examine antipsychotic drugs and heart muscle disorders, a significant association was found between lithium and cardiomyopathy, but not myocarditis (146). The authors acknowledged that further study is needed to determine if the association is causal. 

Space resolution, may require probabilistic rather than deterministic interpretations and analyses. This type of statistical approach is presented in chapters 7 and 8 and in parts of chapter 13, where some elements of Bayesian statistics are introduced. The inference of causality may then be difficult or impossible, as many mechanisms may lead to the same joint probability distribution of all events in the system. 

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

Lane DA, Hutchinson TA, Jones JK, et al. A Bayesian Approach to Causality Assessment. University of Minnesota School of Statistics Tech Reps No 472 (no date available). 

Benichou C, Danan G. Causality assessment in the European pharmaceutical industry presentation of preliminary results of a new method. Drug Inf J 1992 26 589-92. Hutchinson T A. Computerised bayesian ADR assessment. Drug Inf J 1991 25 235 1. Lane DA, Hutchinson TA, Jones JK, Kramer MS, Naranjo CA. A Bayesian Approach to Causality Assessment. Universcity of Minnesota School of Statistics Tech Reps No 472 (no date available). 

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