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Intervening and Letting Go: Understanding Dynamic Causal Models

Weinberger, Naftali (2021) Intervening and Letting Go: Understanding Dynamic Causal Models. [Preprint]

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Abstract

Causal representations are distinguished from non-causal ones by their ability
to predict the results of interventions. This widely-accepted view suggests the following adequacy condition for causal models: a causal model is adequate only if it does not contain variables regarding which it makes systematically false predictions about the results of interventions. Here I argue that this condition should be rejected. For a class of equilibrium systems, there will be two incompatible causal models depending on whether one intervenes upon a certain variable to fix its value, or `lets go' of the variable and allows it to vary. The latter model will fail to predict the result of interventions on the let-go-of
variable. I argue that there is no basis for preferring one of these models to the other, and thus that models failing to predict interventions on particular variables can be just as adequate as those making no such false predictions. This undermines a key argument (Dash, 2003) against relying upon causal models inferred from equilibrium data.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Weinberger, Naftalinaftali.weinberger@gmail.com
Keywords: Causation, Causal Modeling, Dynamical Systems
Subjects: General Issues > Causation
General Issues > Models and Idealization
Depositing User: Mr. Naftali Weinberger
Date Deposited: 02 May 2021 13:41
Last Modified: 02 May 2021 13:41
Item ID: 18980
Subjects: General Issues > Causation
General Issues > Models and Idealization
Date: 2021
URI: http://philsci-archive.pitt.edu/id/eprint/18980

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