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Abstract

A general causal-search canon is presented which, applied to dichotomous data, identifies causes composed of single or multiple factors, extracts maximal information from positive and negative cases, provides refined estimates as cases and variables are processed, and indicates omitted factors. Applications range from clarifying the logic of causality and causal searches in instructional settings to the analysis of research data.

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A Universal Algorithm for Identifying Causes

Abstract

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