Inference to the Best Legal Explanation

Abstract

How do legal decision-makers reason about facts in law? A popular response appeals to probability theory, more specifically, to Bayesian theory. In the Bayesian approach, fact-finders’ inferential task consists of updating the probability of the hypothesis entailing guilt in light of the evidence at trial in the way dictated by Bayes’ theorem. If, by the end of the trial, this probability is sufficiently high to meet the reasonable doubt standard, the verdict ‘guilty’ is appropriate (Tillers and Green 1988). Bayesianism provides an elegant framework for analysing evidentiary reasoning in law. Nonetheless, in the last decades, the Bayesian theory of legal proof has been subjected to severe criticism, which has shed serious doubts upon the possibility of explaining legal reasoning about evidence in Bayesian terms.1 In this chapter, I shall explore the feasibility of an approach to legal evidence and proof alternative to the probabilistic one, to wit, an explanationist approach. According to this approach, many instances of factual reasoning in law are best understood as ‘inferences to the best explanation, ' i.e., a pattern of reasoning whereby explanatory hypotheses are formed and evaluated. More specifically, I shall argue for a coherentist approach to inference to the best explanation for law according to which factual inference in law involves first the generation of a number of plausible alternative explanations of the events being litigated at trial and then the selection, among them, of the one that is best according to a test of explanatory coherence.