A Causal Power Semantics for Generic Sentences
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A Causal Power Semantics for Generic Sentences Robert van Rooij1
•
Katrin Schulz1
The Author(s) 2019
Abstract Many generic sentences express stable inductive generalizations. Stable inductive generalizations are typically true for a causal reason. In this paper we investigate to what extent this is also the case for the generalizations expressed by generic sentences. More in particular, we discuss the possibility that many generic sentences of the form ‘ks have feature e’ are true because (members of) kind k have the causal power to ‘produce’ feature e. We will argue that such an analysis is quite close to a probabilistic based analysis of generic sentences according to which ‘relatively many’ ks have feature e, and that, in fact, this latter type of analysis can be ‘grounded’ in terms of causal powers. We will argue, moreover, that the causal power analysis is sometimes preferred to a correlation-based analysis, because it takes the causal structure that gives rise to the probabilistic data into account. Keywords Generic sentences Causality Semantics Probability
1 Introduction Consider the following two causal claims: (1)
a. John’s throw of a stone caused the bottle to break. b. Aspirin causes headaches to diminish.
Intuitively, these statements operate on different levels: (1a) states a causal relation between two tokens of events, while (1-b) states a causal relation between two types of events. Stating it somewhat differently, (1-a) states what is the actual cause of the breaking of the bottle, while (1-b) talks about causation in a generic fashion: it talks about tendencies. Notice that (1-b) is stated by using a generic sentence. In fact, it seems to express the same content as the following generic sentence: (2)
Aspirin relieves headaches.
& Robert van Rooij [email protected] Katrin Schulz [email protected] 1
But if (2) expresses the same content as (1-b), this strongly suggests that also the generic sentence (2) should be given a causal analysis. The standard way to provide a causal analysis of the actual causation statement (1-a) is as something like the following counterfactual analysis (e.g., Lewis 1973a; Halpern 2016): (i) John threw the stone and the bottle broke, and (ii) had John not thrown the stone, the bottle would not have broken. Such an analysis obviously won’t do for (1-b), and neither will it do for (2). Instead, (1b) and (2) seem to express that particular intakes of Aspirin tend to cause particular states of headache to go away, because of what it is to be Aspirin. Or, as we will say, because of the causal power of Aspirin to relieve headaches. This may look like a mysterious analysis, but we will show how to operationalize it such that it can be turned into a testable statement. The proposal that we will discuss in this paper is that many more generic statements should be given a causal analysis. A causal analysis of (2) is highly natural, because ‘relieve’ is a causal verb. But many other generic statements are stated witho
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