Cyclic quantum causal models

Quantum Common Causes and Quantum Causal Models

John-Mark A. Allen, Jonathan Barrett, Dominic C. Horsman, Ciarán M. Lee, and Robert W. Spekkens Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom Department of Physics, University of Durham, South Road, Durham DH1 3LE, United Kingdom Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, Uni...

Quantum Models of Human Causal Reasoning 1 Running head: QUANTUM MODELS OF HUMAN CAUSAL REASONING Quantum Models of Human Causal Reasoning

Throughout our lives, we are constantly faced with a variety of causal reasoning problems. A challenge for cognitive modelers is developing a comprehensive framework for modeling causal reasoning across different types of tasks and levels of causal complexity. Causal graphical models (CGMs), based on Bayes’ calculus, have perhaps been the most successful at explaining and predicting judgments o...

Material for “ On Causal Discovery with Cyclic Additive Noise Models ”

Discovering Cyclic Causal Models by Independent Components Analysis

We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the dis...

Learning linear cyclic causal models with latent variables

Identifying cause-effect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of the necessary and sufficient conditions for identifiability, a search algorithm that is complete, ...