Fang, Wei and Jiji, Zhang
(2024)
Proportionality, Determinate Intervention Effects, and High-Level Causation.
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Abstract
Stephen Yablo’s notion of proportionality, despite controversies surrounding
it, has played a significant role in philosophical discussions of mental causation and of
high-level causation more generally. In particular, it is invoked in James Woodward’s
interventionist account of high-level causation and explanation, and is implicit in a
novel approach to constructing variables for causal modeling in the machine learning
literature, known as causal feature learning (CFL). In this article, we articulate an
account of proportionality inspired by both Yablo’s account of proportionality and the
CFL account of variable construction. The resulting account has at least three merits.
First, it illuminates an important feature of the notion of proportionality, when it is
adapted to a probabilistic and interventionist framework. The feature is that at the center
of the notion of proportionality lies the concept of “determinate intervention effects.”
Second, it makes manifest a virtue of (common types of) high-level causal/explanatory
statements over low-level ones, when relevant intervention effects are determinate.
Third, it overcomes a limitation of the CFL framework and thereby also addresses a
challenge to interventionist accounts of high-level causation.
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