Causal Explanatory Power

Abstract

Schupbach and Sprenger ([2011]) introduce a novel probabilistic approach to measuring the explanatory power that a given explanans exerts over a corresponding explanandum. Though we are sympathetic to their general approach, we argue that it does not (without revision) adequately capture the way in which the causal explanatory power that c exerts on e varies with background knowledge. We then amend their approach so that it does capture this variance. Though our account of explanatory power is less ambitious than Schupbach and Sprenger’s in the sense that it is limited to causal explanatory power, it is also more ambitious because we do not limit its domain to cases where c genuinely explains e. Instead, we claim that c causally explains e if and only if our account says that c explains e with some positive amount of causal explanatory power. 1Introduction2The Logic of Explanatory Power3Subjective and Nomic Distributions 3.1Actual degrees of belief3.2The causal distribution4Background Knowledge 4.1Conditionalization and colliders4.2A helpful intervention5Causal Explanatory Power 5.1The applicability of explanatory power5.2Statistical relevance ≠ causal explanatory power5.3Interventionist explanatory power5.4E illustrated6Conclusion

Coherence, Explanation, and Hypothesis Selection

“…First, while there is certainly merit to this approach it is not immediately clear that it should be seen as a rival to the approaches based on the various measures that are criticized by Glymour and, even if it is, more detailed argument would be need to show that it is superior to these approaches. Second, manipulation can be incorporated into these approaches (see Eva and Stern [2018]). Finally, while incorporating manipulation seems appropriate for quantifying causal influence, it is not clear that it is the right approach for quantifying explanatory power.…”

Action network: a probabilistic graphical model for social simulation

“…Furthermore, higher granularity does not necessarily mean greater explanatory power. Explanatory depth—the degree to which an explanation allows us to understand some phenomenon 63 —does not necessarily correlate with granularity.…”

How good is an explanation?

“…Also, the current approach does not take into account the potential relevance of manipulations to explanatory goodness. Eva and Stern (2019) have shown how this can be done for Schupbach and Sprenger's measure of explanatory power, so it would be interesting to explore whether a similar approach might be appropriate for the current measure. Nevertheless, as it stands, Good's measure does seem to go a long way to capturing key aspects of explanatory goodness.…”