Conditional Predictions

Abstract

The connection between the probabilities of conditionals and the corresponding conditional probabilities has long been explored in the philosophical literature, but its implementation faces both technical obstacles and objections on empirical grounds. In this paper I first outline the motivation for the probabilistic turn and Lewis' triviality results, which stand in the way of what would seem to be its most straightforward implementation. I then focus on Richard Jeffrey's 'random-variable' approach, which circumvents these problems by giving up the notion that conditionals denote propositions in the usual sense. Even so, however, the random-variable approach makes counterintuitive predictions in simple cases of embedded conditionals. I propose to address this problem by enriching the model with an explicit representation of causal dependencies. The addition of such causal information not only remedies the shortcomings of Jeffrey's conditional, but also opens up the possibility of a unified probabilistic account of indicative and counterfactual conditionals.