On the Testable Implications of Causal Models with Hidden Variables

TL;DR

This paper develops a systematic method to identify functional constraints in causal models with hidden variables, enabling more effective testing and inference of such models from data.

Contribution

It introduces a systematic approach to detect functional constraints in causal models with hidden variables, addressing a gap in existing testing criteria.

Findings

Provides a method to identify functional constraints

Facilitates testing of causal models with hidden variables

Aids in inferring causal models from data

Abstract

The validity OF a causal model can be tested ONLY IF the model imposes constraints ON the probability distribution that governs the generated data. IN the presence OF unmeasured variables, causal models may impose two types OF constraints : conditional independencies, AS READ through the d - separation criterion, AND functional constraints, FOR which no general criterion IS available.This paper offers a systematic way OF identifying functional constraints AND, thus, facilitates the task OF testing causal models AS well AS inferring such models FROM data.