An Interventionist Approach to Causation in Psychology

Campbell, John

doi:10.1093/acprof:oso/9780195176803.003.0005

Cited by 57 publications

(35 citation statements)

References 8 publications

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“…This depends on fleshing out a notion of what it means for a model to depict a causal structure. Here I will adopt some terminology from John Campbell (2006Campbell ( , 2008Campbell ( , 2010 and say that the elements of a model (cognitive or neural) constitute control variables for the behavior of the system as a whole. To call an element a control variable is to analogize it to a dial, knob, lever, or switch on a control panel.…”

Section: Realistic Model Pluralismmentioning

confidence: 99%

Integrative Modeling and the Role of Neural Constraints

Weiskopf¹

2016

Philos. of Sci.

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Neuroscience constrains psychology, but stating these constraints with precision is not simple. Here I consider whether mechanistic analysis provides a useful way to integrate models of cognitive and neural structure. Recent evidence suggests that cognitive systems map onto overlapping, distributed networks of brain regions. These highly entangled networks often depart from stereotypical mechanistic behaviors. While this casts doubt on the prospects for classical mechanistic integration of psychology and neuroscience, I argue that it does not impugn a realistic interpretation of either type of model. Cognitive and neural models may depict different, but equally real, causal structures within the mind/brain.

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Section: Realistic Model Pluralismmentioning

confidence: 99%

Integrative Modeling and the Role of Neural Constraints

Weiskopf¹

2016

Philos. of Sci.

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“…The recent revival of the interventionist account of causation, for example, has shed new light on a variety of issues in the philosophy of causation and explanation, including especially the nature of causal explanation in special and social sciences (Woodward 2003;Steel 2006a;Campbell 2007). Besides interpretive work, the interventionist perspective also underlies a powerful framework for causal modeling and reasoning, known as causal Bayesian networks (e.g., Spirtes et al 1993;Pearl 2000).…”

Section: Introductionmentioning

confidence: 99%

Intervention, determinism, and the causal minimality condition

Zhang¹,

Spirtes

2010

Synthese

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We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning

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“…; consider what would have happened, had just this one belief been different in such-and-such a way….) I actually think we've probably granted way too much by this point, for reasons nicely articulated in Campbell (2006). Never mind.…”

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confidence: 99%