“Standard examples of cause–effect relations include pushes, pulls, motions, collisions, actions, and diseases. The typical features (looser than necessary and sufficient conditions) of causality are as follows: temporal ordering, with causes before effects; sensory–motor–sensory patterns such as kicking a ball; regularities expressed by general rules; manipulations and interventions; statistical dependencies; and causal networks of influence. Causality explains why events happen and why interventions work. From this perspective, causality is recognized by inferences to the best explanation that take into account a range of evidence about temporal patterns, correlations, probabilities, and manipulations. Knowledge of mechanisms is not essential to such inferences, but it helps enormously in cases where the interactions of parts connect a putative cause with an effect.” Naturalizing Logic: How Knowledge of Mechanisms Enhances Inductive Inference by Paul Thagard

“Standard examples of cause–effect relations include pushes, pulls, motions, collisions, actions, and diseases.
 
The typical features (looser than necessary and sufficient conditions) of causality are as follows: temporal ordering, with causes before effects; sensory–motor–sensory patterns such as kicking a ball; regularities expressed by general rules; manipulations and interventions; statistical dependencies; and causal networks of influence.
 
Causality explains why events happen and why interventions work.
 
From this perspective, causality is recognized by inferences to the best explanation that take into account a range of evidence about temporal patterns, correlations, probabilities, and manipulations.
 
Knowledge of mechanisms is not essential to such inferences, but it helps enormously in cases where the interactions of parts connect a putative cause with an effect.”
 
Naturalizing Logic: How Knowledge of Mechanisms Enhances Inductive Inference by Paul Thagard

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