# Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities

## TL;DR

This paper introduces new methods to infer causal relationships from data by using information-theoretic constraints, even when some variables are hidden.

## Contribution

The paper expands the framework for groups-decomposition inequalities with weaker assumptions and introduces new constraints using unique information measures.

## Key findings

- New groups-decomposition inequalities are derived with weaker independence and configuration requirements.
- Constraints with higher inferential power can be derived using hidden variables and data processing inequalities.
- An analogous data processing inequality is derived for a measure of conditional unique information.

## Abstract

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables. However, particularly in the presence of hidden variables, many causal structures are compatible with the same set of independencies inferred from the marginal distributions of observed variables. Additional constraints allow further testing for the compatibility of data with specific causal structures. An existing family of causally informative inequalities compares the information about a set of target variables contained in a collection of variables, with a sum of the information contained in different groups defined as subsets of that collection. While procedures to identify the form of these groups-decomposition inequalities have been previously derived, we substantially enlarge the applicability of the framework. We derive groups-decomposition inequalities subject to weaker independence conditions, with weaker requirements in the configuration of the groups, and additionally allowing for conditioning sets. Furthermore, we show how constraints with higher inferential power may be derived with collections that include hidden variables, and then converted into testable constraints using data processing inequalities. For this purpose, we apply the standard data processing inequality of conditional mutual information and derive an analogous property for a measure of conditional unique information recently introduced to separate redundant, synergistic, and unique contributions to the information that a set of variables has about a target.