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Causalists about explanation claim that to explain an event is to provide information about the causal history of that event. Some causalists also endorse a proportionality claim, namely that one explanation is better than another insofar as it provides a greater amount of causal information. In this chapter I consider various challenges to these causalist claims. There is a common and influential formulation of the causalist requirement — the ‘Causal Process Requirement’ — that does appear vulnerable to these anti‐causalist challenges, but I argue that they do not give us reason to reject causalism entirely. Instead, these challenges lead us to articulate the causalist requirement in an alternative way. This alternative articulation incorporates some of the important anti‐ causalist insights without abandoning the explanatory necessity of causal information. For example, proponents of the ‘equilibrium challenge’ argue that the best available explanations of the behaviour of certain dynamical systems do not appear to provide any causal information. I respond that, contrary to appearances, these equilibrium explanations are fundamentally causal, and I provide a formulation of the causalist thesis that is immune to the equilibrium challenge. I then show how this formulation is also immune to the ‘epistemic challenge’ — thus vindicating (a properly formulated version of) the causalist thesis.

The causal power of C over E is (roughly) the degree to which changes in C cause changes in E. A formal measure of causal power would be very useful, as an aid to understanding and modelling complex stochastic systems. Previous attempts to measure causal power, such as those of Good (1961), Cheng (1997), and Glymour (2001), while useful, suffer from one fundamental flaw: they only give sensible results when applied to very restricted types of causal system, all of which exhibit causal transitivity. Causal Bayesian networks, however, are not in general transitive. The chapter develops an information‐theoretic alternative, causal information, which applies to any kind of causal Bayesian network. Causal information is based upon three ideas. First, the chapter assumes that the system can be represented causally as a Bayesian network. Second, the chapter uses hypothetical interventions to select the causal from the non‐causal paths connecting C to E. Third, we use a variation on the information‐theoretic measure mutual information to summarize the total causal influence of C on E. The chapter's measure gives sensible results for a much wider variety of complex stochastic systems than previous attempts and promises to simplify the interpretation and application of Bayesian networks.

This chapter outlines a theory grounded on the so-called attentional constitution principle (ACP). The ACP contends that attention is constitutive of humans’ perceptual knowledge about individuals. It expands research on perception and demonstrative identification, and is grounded in the idea that the epistemology of empirical beliefs should fit together with the psychobiology of attention in order to explain how human agents navigate and analyze their environment. In contrast to the nonbiological epistemology of knowledge or the nonepistemological psychobiology of attention, the ACP holds that the function of human attention is mainly to serve perceptual knowledge through the extraction of causal information. The following sections of the chapter formulate the ACP and introduce a concept of information that is useful and relevant to the theory.