Non-parametric causal inference for bivariate time series

TL;DR

This paper proposes new, model-free quantities called penchants and leanings for causal inference in bivariate time series, offering a straightforward and interpretable alternative to existing methods.

Contribution

It introduces penchants and leanings, novel quantities for causal inference that do not depend on models or embeddings, enhancing interpretability and applicability.

Findings

Quantities are computationally straightforward.

Method does not rely on embedding procedures.

Provides clearer interpretation of causal relationships.

Abstract

We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.