Causal History, Statistical Relevance, and Explanatory Power

Abstract

AbstractIn discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal explanations are powerful to the extent that the occurrence of a cause allows us to predict the occurrence of its effect. This article proves a representation theorem showing that there is a unique family of functions measuring a causal explanation’s power that satisfies these two premises.