Is everyday causation deterministic or probabilistic?

Area of Science:

• Cognitive Psychology
• Philosophy of Science
• Causal Inference

Background:

• Causation can be viewed as deterministic (A always leads to B) or probabilistic (A increases the likelihood of B).
• Distinguishing between these views is challenging based solely on evidence of causal induction.
• Understanding how individuals refute causal assertions provides insight into their underlying conceptualization of causation.

Purpose of the Study:

• To investigate how people refute causal assertions, differentiating between deterministic and probabilistic interpretations.
• To test predictions derived from a mental models theory of causal reasoning.
• To determine whether everyday concepts of causation align more with deterministic or probabilistic accounts.

Main Methods:

• Conducted four experiments examining participants' refutation strategies for causal assertions.
• Presented participants with assertions of the form 'A causes B' and 'A enables B'.
• Analyzed the types and number of counterexamples participants used to refute these assertions.

Main Results:

• A majority of participants considered a single counterexample (A occurring without B) sufficient to refute 'A causes B' assertions.
• Participants were more inclined to seek multiple refutations for 'A enables B' assertions, consistent with deterministic predictions.
• Refutations involving 'not-A and B' were more common for enabling claims than for causal claims.

Conclusions:

• Everyday understanding of causation appears to be predominantly deterministic.
• Mental models theory effectively predicts how individuals use counterexamples to evaluate causal and enabling relationships.
• The findings suggest a cognitive bias towards interpreting causal links as absolute rather than probabilistic.