When evaluating theories of causation, intuitions should not play a decisive role, not even intuitions in flawlessly-designed thought experiments. Indeed, no coherent theory of causation can respect the typical person’s intuitions in redundancy (pre-emption) thought experiments, without disrespecting their intuitions in threat-and-saviour (switching/short-circuit) thought experiments. I provide a deductively sound argument for these claims. Amazingly, this argument assumes absolutely nothing about the nature of causation. I also provide a second argument, whose conclusion is even stronger: the typical person’s causal intuitions (...) are thoroughly unreliable. This argument proceeds by raising the neglected question: in what respects is information about intermediate and enabling variables relevant to reliable causal judgment? (shrink)

This paper is about what I call the Question of Iterated Causation (QIC): for any instance of causation in which c1…ck cause effect e, what are the causes of c1…ck’s causing of e? In short: what causes instances of causation or, as I will refer to these instances, the “causal goings‐on”? A natural response (which I call “dismissivism”) is that this is a bad question because causal goings‐on aren’t apt to be caused. After rebutting several versions of dismissivism, I consider (...) the view that QIC, though not illegitimate, is easy to answer: the causal goings‐on are apt to be caused but are plainly uncaused (“brutism”). However, I will argue that brutism too has a serious problem: namely, it leads to a highly implausible kind of armchair indeterminism. Next I consider some substantive candidate answers to QIC, none of which, I argue, is particularly promising. The paper’s final conclusion is twofold: QIC is at least as difficult as the more well‐known Question of Iterated Grounding; moreover, the largely overlooked regress problem that it raises gives us at least some defeasible reason to avoid causation in theory‐building. (shrink)

This thesis will survey several prominent approaches to analysing causation, discuss their differences and similarities, and look at a number of problems which are common to all of them. I will be arguing for the following claims about how we should approach the process of analysing causation. Firstly, I will be arguing that a reductive analysis is desirable, since if we can reductively analyse causation in terms of something empirically accessible, we can explain how it is possible to know anything (...) about causation. I will argue that to reductively analyse causation is to find out what kind of facts ground causal facts. Secondly, I will argue, following Hall and Strevens, that there are two kinds of causation, causal difference making and causal influence. This two-tiered approach explains the cases where we are tempted to ascribe conflicting characteristics to our concept of causation. Thirdly, I will argue that causal influence grounds causal difference making and that it does so necessarily. That the grounding relation holds necessarily is important for defending the two-tiered approach against the objection that it would yield a disjunctive account. (shrink)

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...) an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

I develop an account of counterfactual conditionals using “causal models”, and argue that this account is preferable to the currently standard account in terms of “similarity of possible worlds” due to David Lewis and Robert Stalnaker. I diagnose the attraction of counterfactual theories of causation, and argue that it is illusory.

In this article I offer an approach to counterfactuals based on a notion of objective probability. It is in the spirit of, though it does not fall squarely under, the metalinguistic model. Thus, it is not developed in terms of possible worlds, or notions parasitic on them (e.g., similarity). Its dominant features are rooted in objective probability and causal relevance (analyzed probabilistically), and thus it is not close in spirit to a maximal similarity or a minimal change approach.

In this paper I explore the ambiguity that arises between two readings of the counterfactual construction, then–d and thel–p, analyzed in my bookA Theory of Counterfactuals. I then extend the analysis I offered there to counterfactuals with true antecedents, and offer a more precise formulation of the conception of temporal divergence points used in thel–p interpretation. Finally, I discuss some ramifications of these issues for counterfactual analyses of knowledge.

In Kvart (1991a), I discussed the analysis of causal relevance presented in A Theory of Counterfactuals (1986) (and first in 1975). I explained there in what respect the notion captured by the analysis of Kvart (1986) is a mere approximation to the requisite notion of causal relevance. In this paper I present another analysis of causal relevance, devoid of the shortcoming of its predecessor. The present analysis of causal relevance is, again, grounded in a chancelike notion of objective probability. The (...) correlative notion of causal independence is analyzed as holding in case there is a so-called causal impact series, which is defined here. The basic notions used for this analysis are the notion of a differentiator, which upsets an equiprobability case, and that of a blocker, which restores it. (shrink)

It has received little attention that the truth values of sequential counterfactuals (i.e. counterfactuals in which the antecedent event precedes the consequent event) can shift over time. In the footsteps of Goodman, I develop a theory of sequential counterfactuals that can account for this feature. The theory (i) defends a semifactual test of cotenability, (ii) spells out the truth conditions for semifactuals in probabilistic terms and (iii) accounts for truth-value shifts by appealing to a dynamic view of time.