This paper responds to recent criticisms of the idea that true causal claims, satisfying a minimal “interventionist” criterion for causation, can differ in the extent to which they satisfy other conditions—called stability and proportionality—that are relevant to their use in explanatory theorizing. It reformulates the notion of proportionality so as to avoid problems with previous formulations. It also introduces the notion of conditional independence or irrelevance, which I claim is central to understanding the respects and the extent to which upper (...) level explanations can be “autonomous”. (shrink)

Proportionality—the idea that causes are neither too general nor too specific for their effects—seems to recommend implausibly disjunctive causes (McGrath, 1998 ; Shapiro & Sober, 2012 ; Franklin-Hall, 2016 ). I argue that this problem should be avoided by appeal to the notion of cohesion. I propose an account of cohesion in terms of the similarity structure of property-spaces, argue that it is not objectionably mysterious, and that alternative approaches—based on naturalness, interventionism, and contrastivism—are inadequate without appeal to it. In (...) an appendix, I show how my proposal can be perspicuously formalized by adapting structural equation models. (shrink)

According to the so-called 'proportionality principle', causes should be proportional to their effects: they should be both enough and not too much for the occurrence of their effects. This principle is the subject of an ongoing debate. On the one hand, many maintain that it is required to address the problem of causal exclusion and take it to capture a crucial aspect of causation. On the other hand, many object that it renders accounts of causation implausibly restrictive and often reject (...) the principle wholesale. I argue that there is exaggeration on both sides. While one half of the principle is overly demanding, the other half is unobjectionable. And while the unobjectionable half does not block exclusion arguments on its own, it provides a nuanced picture of higher-level causation, fits with recent developments in philosophy of causation, and motivates adjustments to standard difference-making accounts of causation. I conclude that at least half of the proportionality principle is worth taking seriously. (shrink)

Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader in (...) scope and/or more accurate is better. By applying these definitions and contrasting them with alternative definitions in the XAI literature I hope to help clarify what a good explanation is for AI. (shrink)

This dissertation begins by addressing the question of when a causal model is apt for deciding questions of actual causation with respect to some target situation. I first provide relevant background about causal models, explain what makes them promising as a tool for analyzing actual causation, and motivate the need for a theory of aptness as part of such an analysis (Chapter 1). I then define what it is for a model on a given interpretation to be accurate of, that (...) is, say only true things about, some target situation. This involves a systematization of various representational principles mentioned and/or discussed throughout the literature into a method of interpretation, which I propose be taken as standard (Chapter 2). Next, I explain and address two reasons for which accuracy as I’ve defined it is insufficient for aptness. The first reason – already discussed in the literature – is the problem of structural isomorphs. In response, I propose the aptness condition of Explicit Partial Mediation (Chapter 3). The second reason – which has yet to be noticed – is the problem of the indeterminacy of accuracy. As I demonstrate, a model is accurate of a target situation only relative to a set of background possibilities – what I call a modal profile. It follows that a model represents a situation only relative to some modal profile or other. I go on to discuss the ramifications of this observation for a theory of actual causation in terms of models. I argue that the relativity be taken at face value and built into our metaphysical account of causation, resulting in a view that I call causal relativism (Chapter 4). I explore one advantage of this view in detail: that the resulting account can defend the principle of strong proportionality against several objections (Chapter 5). Finally, I apply the earlier discussion of aptness to attempts to provide a semantics of counterfactuals in terms of causal models – an interventionist semantics. I show how just as a similarity semantics relies on an opaque notion of similarity, an interventionist semantics relies on an analogous notion of aptness. The challenge of articulating aptness thus undermines the claim that an interventionist semantics avoids representational problems inherent in a similarity semantics (Chapter 6). I close with a recap and suggestions for future research (Chapter 7). -/- . (shrink)

In ‘Proportionality in Causation, Part I: Theories’, I presented various ways of understanding the idea that causes which are ‘proportional’ to their effects are in some sense preferable. In this companion article, I discuss the principal applications of the resulting theories of proportionality, and the challenges they face.

Biologists and philosophers of science have recently called for an extension of evolutionary theory. This so-called ‘extended evolutionary synthesis’ seeks to integrate developmental processes, extra-genetic forms of inheritance, and niche construction into evolutionary theory in a central way. While there is often agreement in evolutionary biology over the existence of these phenomena, their explanatory relevance is questioned. Advocates of EES posit that their perspective offers better explanations than those provided by ‘standard evolutionary theory’. Still, why this would be the case (...) is unclear. Usually, such claims assume that EES’s superior explanatory status arises from the pluralist structure of EES, its different problem agenda, and a growing body of evidence for the evolutionary relevance of developmental phenomena. However, what is usually neglected in this debate is a discussion of what the explanatory standards of EES actually are, and how they differ from prevailing standards in SET. In other words, what is considered to be a good explanation in EES versus SET? To answer this question, we present a theoretical framework that evaluates the explanatory power of different evolutionary explanations of the same phenomena. This account is able to identify criteria for why and when evolutionary explanations of EES are better than those of SET. Such evaluations will enable evolutionary biology to find potential grounds for theoretical integration. (shrink)

The Ethical Question asks whether ethical values in artworks determine their aesthetic value and, if so, how. I argue that the question is ambiguous between a direct and an indirect reading. I show how the indirect reading is philosophically uninteresting because it has an obvious answer: a view called ‘immoralism’. I also show how most of the significant figures in the relevant literature address the indirect form of the question anyway—needlessly, if I am right. Finally, I consider whether some version (...) of the indirect question is more philosophically interesting, connecting it to the so-called ‘qua problem’. I attempt to give clarity to the discussion by applying work on the virtues and vices of explanations. (shrink)

In this dissertation, I develop and defend a model of causation that allows for dualist mental causation in worlds where the physical domain is physically complete. In Part I, I present the dualist ontology that will be assumed throughout the thesis and identify two challenges for models of mental causation within such an ontology: the exclusion worry and the common cause worry. I also argue that a proper response to these challenges requires a thoroughly lightweight account of causation, i.e. an (...) account that allows for causes to be metaphysically distinct from the phenomena that produce or physically necessitate their effects. In Part II, I critically evaluate contemporary responses to these challenges from the philosophical literature. In particular, I discuss List and Stoljar’s criticism of exclusion worries, Kroedel’s alternative dualist ontology, concerns about the notion of causal sufficiency, and Lowe’s models of dualist mental causation. I argue that none of these proposals provide independent motivation for a thoroughly lightweight account of causation and therefore leave room for improvement. In the first four chapters of Part III, I develop a thoroughly lightweight model of causation, which builds on interventionist approaches to causation. First, I explain how so-called ‘holding fixed’-requirements in standard interventionist accounts stand in the way of dualist mental causation. I then argue that interventionist accounts should impose a robustness condition on causal correlations and that, with this condition in place, the ‘holding fixed’-requirements can be weakened such that they do allow for dualist mental causation. I dub the interventionist model with such weakened ‘holding fixed’-requirements ‘insensitive interventionism’, argue that it can counter the exclusion worry as well as the common cause worry, and explain under which circumstances it would predict there to be dualist mental causation. Importantly, these circumstances might, for all we know, hold in the actual world. In the final three chapters of Part III, I defend insensitive interventionism against some objections. I consider the objection that causation must be productive, the objection that causes must physically necessitate their effects, and the objection that insensitive interventionism is too permissive. I respond by drawing from the literature on causation by absences and on the relation between causation and fundamental physics. Overall, insensitive interventionism performs as well as standard interventionist accounts. I conclude that insensitive interventionism is a credible model of causation. (shrink)

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. (shrink)

Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is (...) generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents. (shrink)

There are several supposedly lethal objections to the view that causation is essentially proportional. The first targets an account of proportionality in terms of causal models, pointing out that proportionality is too easily satisfied in causal model accounts of causation through manipulation of the range of values that a variable can take (Franklin-Hall, 2016). The second argues that proportionality legitimizes only the most general things as causes, and proportionality thereby contravenes causal intuitions (Bontly, 2005; Franklin-Hall, 2016; McDonnell, 2018, 2017; Weslake, (...) 2013). The final, and perhaps most intractable, objection holds that proportionality counter-intuitively legitimizes disjunctive causes (Shapiro and Sober, 2012; Weslake, 2017; Woodward, 2018). This paper provides a unified response to these objections, which is best formulated in a causal model framework. I first articulate two independently plausible principles of variable selection – exclusivity and exhaustivity. I then show how the adoption of these principles responds to Franklin-Hall’s objection, and dissolves the remaining two. (shrink)