Carl Hempel (1965) argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon (1971, 1984, 1990, 1998) and Richard Jeffrey (1969) argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal (...) explanation is described and defended. It provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...) adequacy conditions. The paper concludes by sketching future research avenues. (shrink)

Invariance is one of several dimensions of causal relationships within the interventionist account. The more invariant a relationship between two variables, the more the relationship should be considered paradigmatically causal. In this paper, I propose two formal measures to estimate invariance, illustrated by a simple example. I then discuss the notion of invariance for causal relationships between non-nominal (i.e., ordinal and quantitative) variables, for which Information theory, and hence the formalism proposed here, is not well suited. Finally, I propose how (...) invariance could be qualified for such variables. (shrink)

The interventionist account provides us with several notions permitting the qualification of causal relationships. In recent years, there has been a push toward formalizing these notions using information theory. In this paper, I discuss one of them, namely causal specificity. The notion of causal specificity is ambiguous as it can refer to at least two different concepts. After having presented these, I show that current attempts to formalize causal specificity in information theoretic terms have mostly focused on one of these (...) two concepts. I then propose and apply a new information-theoretic measure which captures the other concept. (shrink)

Heritability is routinely interpreted causally. Yet, what such an interpretation amounts to is often unclear. Here, I provide a causal interpretation of this concept in terms of range of causal influence, one of several causal dimensions proposed within the interventionist account of causation. An information-theoretic measure of range of causal influence has recently been put forward in the literature. Starting from this formalization and relying upon Woodward’s analysis, I show that an important problem associated with interpreting heritability causally, namely the (...) locality problem, amounts, at least partly, to a low invariance and low stability between the genotype/environment and the phenotype of individuals. In light of this, I plead for a causal interpretation of heritability that takes the notions of Woodward’s invariance and stability into consideration. In doing so, I defuse naive causal interpretations of heritability. (shrink)

Carl Hempel argued that probabilistic hypotheses are limited in what they can explain. He contended that a hypothesis cannot explain why E is true if the hypothesis says that E has a probability less than 0.5. Wesley Salmon and Richard Jeffrey argued to the contrary, contending that P can explain why E is true even when P says that E’s probability is very low. This debate concerned noncontrastive explananda. Here, a view of contrastive causal explanation is described and defended. It (...) provides a new limit on what probabilistic hypotheses can explain; the limitation is that P cannot explain why E is true rather than A if P assign E a probability that is less than or equal to the probability that P assigns to A. The view entails that a true deterministic theory and a true probabilistic theory that apply to the same explanandum partition are such that the deterministic theory explains all the true contrastive propositions constructable from that partition, whereas the probabilistic theory often fails to do so. (shrink)

The interventionist account provides us with several notions permitting the qualification of causal relationships. In recent years, there has been a push toward formalizing these notions using information theory. In this paper, I discuss one of them, namely causal specificity. The notion of causal specificity is ambiguous as it can refer to at least two different concepts. After having presented these, I show that current attempts to formalize causal specificity in information theoretic terms have mostly focused on one of these (...) two concepts. I then propose and apply a new information-theoretic measure which captures the other concept. (shrink)