Causal Markov condition for submodular information measures

TL;DR

This paper generalizes the causal Markov condition to arbitrary submodular information measures, enabling causal inference beyond Shannon entropy and Kolmogorov complexity, with practical experiments on real data.

Contribution

It introduces a generalized CMC framework for submodular information measures, providing computable alternatives to Kolmogorov complexity for causal inference.

Findings

Effective causal inference using compression-based measures.

Theoretical justification for the generalized CMC.

Successful experiments on real data demonstrating the approach.

Abstract

The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a causal hypothesis in terms of a directed acyclic graph. In the conventional setting, the observations are random variables and the independence is a statistical one, i.e., the information content of observations is measured in terms of Shannon entropy. We formulate a generalized CMC for any kind of observations on which independence is defined via an arbitrary submodular information measure. Recently, this has been discussed for observations in terms of binary strings where information is understood in the sense of Kolmogorov complexity. Our approach enables us to find computable alternatives to Kolmogorov complexity, e.g., the length of a text after applying existing data compression schemes. We show that our CMC is justified if one restricts the attention to a class of causal mechanisms that is adapted to the respective information measure. Our justification is similar to deriving the statistical CMC from functional models of causality, where every variable is a deterministic function of its observed causes and an unobserved noise term. Our experiments on real data demonstrate the performance of compression based causal inference.