On causally asymmetric versions of Occam's Razor and their relation to thermodynamics

Understand

In real-life statistical data, it seems that conditional probabilities for the effect given their causes tend to be less complex and smoother than conditionals for causes, given their effects.

• We have recently proposed and tested methods for causal inference in machine learning using a formalization of this principle.
• Here we try to provide some theoretical justification for causal inference methods based upon such a ``causally asymmetric'' interpretation of Occam's Razor.
• To this end, we discuss toy models of cause-effect relations from classical and quantum physics as well as computer science in the context of various aspects of complexity.