Flagpoles anyone? Causal and explanatory asymmetries
James Woodward · THEORIA An International Journal for Theory History and Foundations of Science · 2020
This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.