Frequentist and Bayesian Causal Inference in Tests of Hypotheses

Abstract

The previous chapter dealt with challenges that one confronts in collecting and using evidence for the generation of causal inferences. The challenges and means to address them pertain to case studies that build hypothesis, test them, or seek to modify them in order to make sense of puzzling cases. A separate and important topic reserved for this chapter concerns frequentist and Bayesian causal inference as two ways of producing inferences in tests of cross-case and within-case hypotheses. Frequentist causal inference is based on the number of observations and the premise that the more supportive or disconfirming observations one collects, the stronger causal inferences are. Bayesianism emphasizes the theoretical impact and likelihood of collecting individual observations as opposed to their number. Among other things, a discussion of these two modes of causal inferences closes the circle with respect to Chapter 3. As is detailed below, distribution-based case selection is integral to frequentist causal inference, whereas Bayesianismrelies on the theory-based choice of cases.KeywordsCausal InferenceLabor UnionConditional LikelihoodDecisive TestHigh CertaintyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.