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This work argues that concepts are not stable monolithic entities, but rather are dynamic loose networks of interconnected knowledge elements that can rearrange in response to context and are composed of explanatory primitives (some that have a clear somatic basis) that can bear little resemblance to expert causal thinking.
"Causal thinking evolved because it allows people to understand and control their environment, i.e. to be able to predict that, for example, if you eat a red mushroom you will die," writes an Oxford University Press psychology textbook.
Causal thinking in judgment under uncertainty.
A bit of causal thinking can go a long way.
Support for different policy alternatives corresponds with different causal thinking.
Without language, concepts, long-term causal thinking, she can still enter into the large part of our mind made up of appetites, longings, and loyalties.
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Before we discuss instances of these in the literature, let us see another causal form that a need-based explanation takes in the student's thinking.
For such reasons, he rejects Descartes's causal interactionism, where thinking substance is said to cause changes in an independent material substance and vice-versa.
What is so nice about this picture is that if LOTH is true we have a naturalistically adequate causal treatment of thinking that respects the semantic properties of the thoughts involved: it is in virtue of the physically coded syntactic/formal features that thoughts cause each other while the coherence of their semantic properties is preserved precisely in virtue of this.
These are a complex set of issues that hinge upon both causal and counterfactual thinking.
I suggest that instead the gold standard we should use for thinking about causal inference in epidemiology is the thought experiment that, for example, compares an actual disease frequency under one exposure level with a counterfactual disease frequency under a different exposure level (as discussed in Greenland and Robins (1986) and Maldonado and Greenland (2002)).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com