Publications

In recent years the notion of biological specificity has attracted significant philosophical attention. This paper focuses on host specificity, a kind of biological specificity that has not yet been discussed by philosophers, and which concerns the extent to which a species is selective in the range of other species it exploits for feeding and/or reproduction. Host specificity is an important notion in ecology, where it plays a variety of theoretical roles. Here I focus on the role of host specificity in biological control, a field of applied ecology that deals with the suppression of pests through the use of living organisms. Examining host specificity and its role in biological control yields several valuable contributions to our understanding of biological specificity. In particular, I argue that host specificity cannot be fully understood in terms of Woodward’s well-known account of causal specificity.

An important question for the causal modeling approach is how to integrate non-causal dependence relations such as asymmetric supervenience into the approach. The most prominent proposal to that effect (due to Gebharter) is to treat those dependence relationships as formally analogous to causal relationships. We argue that this proposal neglects some crucial differences between causal and non-causal dependencies, and that in the context of causal modeling non-causal dependence relationships should be represented as mutual dependence relationships. We develop a new kind of model – ‘hybrid models’ - based on this suggestion, and formulate a set of axioms for those models. Our formalism has important implications for Kim’s exclusion problem: whereas Gebharter’s framework vindicates Kim’s causal exclusion objection against nonreductive physicalism, our framework has no such implication, and can help non-reductive physicalists vindicate the efficacy of high-level properties. A further benefit of our formalism is that it yields a natural and plausible way of thinking about interventions in multi-level contexts.

This paper argues that the knowledge asymmetry (the fact that we know more about the past than the future) can be explained as a consequence of the causal Markov condition. The causal Markov condition implies that causes of a common effect are generally statistically independent, whereas effects of a common cause are generally correlated. I show that together with certain facts about the physics of our world, the statistical independence of causes severely limits our ability to predict the future, whereas correlations between joint effects make it so that no such limitation holds in the reverse temporal direction. Insofar as the fact that our world conforms to the causal Markov condition can itself be explained in terms of the initial conditions of the universe, my view is compatible with Albert’s well-known account of the origins of temporal asymmetries, but also provides a more illuminating way to derive the knowledge asymmetry from those initial conditions.

This paper argues that the interventionist account of causation faces a dilemma concerning macroscopic causation – i.e., causation by composite objects. Interventionism must either require interventions on a composite object to hold the behavior of its parts fixed, or allow such interventions to vary the behavior of those parts. The first option runs the risk of making wholes causally excluded by their parts, while the second runs the risk of mistakenly ascribing to wholes causal abilities that belong to their parts only.

Intuitions play an important role in the debate on the causal status of high-level properties. For instance, Kim has claimed that his ’exclusion argument’ relies on ‘a perfectly intuitive understanding of the causal relation’. We report the results of three experiments examining whether laypeople really have the relevant intuitions. We find little support for Kim’s view and the principles on which it relies.

The epidemiologist Bradford Hill famously argued that in epidemiology, specificity of association (roughly, the fact that an environmental or behavioral risk factor is associated with just one or at most a few medical outcomes) is strong evidence of causation. Prominent epidemiologists have dismissed Hill’s claim on the ground that it relies on a dubious ‘one-cause one effect’ model of disease causation. The paper examines this methodological controversy, and argues that specificity considerations do have a useful role to play in causal inference in epidemiology.

Theories of explanation need to account for a puzzling feature of our explanatory practices: the fact that we prefer explanations that are relatively abstract but only moderately so. I argue (against Franklin-Hall) that the interventionist account of explanation provides a natural and elegant explanation of this fact. By striking the right balance between specificity and generality, moderately abstract explanations optimally subserve what interventionists regard as the goal of explanation, namely identifying possible interventions that would have changed the explanandum.

Occam’s razor plays a prominent role in ordinary and scientific inference. One attractive hypothesis known as Bayesian Occam’s razor (BOR) is that more complex hypotheses tend to be more flexible and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people’s intuitive probabilistic and explanatory judgments follow the prescriptions of BOR.

Proponents of IBE claim that the ability of a hypothesis to explain a range of phenomena in a unifying way contributes to the hypothesis’s credibility in light of these phenomena. I propose a Bayesian justification of this claim that reveals a hitherto unnoticed role for explanatory unification in evaluating the plausibility of a hypothesis.

Much recent work on explanation in the interventionist tradition emphasizes the explanatory value of stable causal generalizations. We argue that two separate explanatory virtues are lumped together under the heading of ‘stability’. We call these two virtues breadth and guidance respectively.

We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances.

More than a century ago, Russell launched a forceful attack on causation. He thereby initiated a debate about the relations between physics and causation that remains very much alive today.

I raise two issues for Machery’s discussion and interpretation of the theory-theory.

In recent years, an active research program has emerged that aims to develop a Humean best-system account (BSA) of laws of nature that improves on Lewis’s canonical articulation of the view. Its guiding idea is that the laws are cognitive tools tailored to the specific needs and limitations of creatures like us. While current versions of this ‘pragmatic Humean’ research program fare much better than Lewis’s account along many dimensions, I will argue that they have trouble making sense of certain key features of the practice of fundamental physics.

Menzies, Hitchcock, Hall, and Halpern have argued that standard causal models must be supplemented with a distinction between default and deviant events. We critically evaluate this proposal. We grant that the notions of ‘default’ and ‘deviant’ influence causal judgement, but we claim that this influence is best understood as arising through a general cognitive bias concerning the availability of alternatives.