A Theory of Diagnostic Inference: Judging Causality.
Technical rept. no. 4,
CHICAGO UNIV IL CENTER FOR DECISION RESEARCH
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Diagnostic inference is concerned with determining the causal process that produced a set of outcomesresultssymptoms. A model of causal reasoning within diagnosis is presented. We first propose that people use a sequential anchor-and-adjust strategy in discounting an explanation by alternatives. The amount of discounting depends on three factors the plausibility of alternatives, the initial strength of the hypothesis, and a parameter reflecting the weight given to disconfirmatory evidence. It is then shown that the strength of a causal explanation is highly dependent on an implicit causal background as in figureground relations, and on probabilistic factors called cues-to-causality. The cues considered are temporal order, contiguity, covariation, and similarity of cause and effect. A model for weighting and combining the cues is shown to account for much research in a wide range of fields. The three components of the theory are then tested in a series of experiments and the results are discussed with respect to the factors that affect the discounting of explanations issues in combining the cues-to-causality problems in defining the causal background and normative questions in assessing the quality of causal judgments.