PIRSA:18040124

Causal inference rules for algorithmic dependences and why they reproduce the arrow of time

Janzing, D. (2018). Causal inference rules for algorithmic dependences and why they reproduce the arrow of time. Perimeter Institute. https://pirsa.org/18040124

Janzing, Dominik. Causal inference rules for algorithmic dependences and why they reproduce the arrow of time. Perimeter Institute, Apr. 12, 2018, https://pirsa.org/18040124 

          @misc{ pirsa_PIRSA:18040124,
            doi = {10.48660/18040124},
            url = {https://pirsa.org/18040124},
            author = {Janzing, Dominik},
            keywords = {Quantum Foundations},
            language = {en},
            title = {Causal inference rules for algorithmic dependences and why they reproduce the arrow of time},
            publisher = {Perimeter Institute},
            year = {2018},
            month = {apr},
            note = {PIRSA:18040124 see, \url{https://pirsa.org}}
          }

Dominik Janzing

Max Planck Institute for Biological Cybernetics

Talk number
PIRSA:18040124
Collection
Talk Type
Subject
Abstract
The causal Markov condition relates statistical dependences to causality. Its relevance is meanwhile widely appreciated in machine learning, statistics, and physics. I describe the *algorithmic* causal Markov condition relating algorithmic dependences to causality, which can be used for inferring causal relations among single objects without referring to statistics. The underlying postulate "no algorithmic dependence without causal relation" extends Reichenbach's Principle to a probability-free setting. I argue that a related postulate called "algorithmic independence of initial state and dynamics" reproduces the non-decrease of entropy according to the thermodynamic arrow of time.