Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

The heart of the scientific enterprise is a rational effort to understand the causes behind the
phenomena we observe. In large-scale complex dynamical systems such as the Earth …

Causal network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …

Conditional independence testing is a fundamental problem underlying causal discovery
and a particularly challenging task in the presence of nonlinear dependencies. Here a fully …

We propose a coefficient of conditional dependence between two random variables Y and Z
given a set of other variables X 1,…, X p, based on an iid sample. The coefficient has a long …

We propose a general new method, the conditional permutation test, for testing the
conditional independence of variables X and Y given a potentially high dimensional random …

Abstract Conditional Mutual Information (CMI) is a measure of conditional dependence
between random variables X and Y, given another random variable Z. It can be used to …

Machine learning (ML) is now widespread. Traditional software engineering can be applied
to the development ML applications. However, we have to consider specific problems with …

This paper focuses on nonparametric procedures for testing conditional independence
between random vectors using Möbius transformation. We derive a method predicated on …

Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …