# Causal Conditional Reasoning and Conditional Likelihood

- Philip Fernbach,
*Brown University*
- Adam Darlow,
*Brown University*

## Abstract

We hypothesized that causal conditional reasoning reflects
judgment of the conditional likelihood of causes and effects based on a
probabilistic causal model of the scenario being judged. Although this proposal
has much in common with Cummins’ (1995) theory based on the number of
disabling conditions and alternative causes, it takes more variables into account
and therefore makes some differing predictions. To test this idea we collected
judgments of the causal parameters of the conditionals and used them to derive
predictions from a model with zero free parameters. We compared these predictions
to Cummins’ acceptability ratings and to analogous likelihood judgments that
we also collected. The hypothesis was borne out for Affirming the Consequent and
the analogous diagnostic likelihood judgments, where the model obtained close
fits to both data sets. However, we found a surprising dissociation between Modus
Ponens and judgments of predictive likelihood leading to a relatively poor fit to
the Modus Ponens acceptability ratings. We propose an explanation for this in the
discussion.

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