Detecting Causal Relations in the Presence of Unmeasured Variables

TL;DR

This paper presents theorems that identify conditions allowing reliable inference of causal relations between two measured variables despite the presence of unmeasured latent variables.

Contribution

It introduces new theorems that specify when causal directions can be determined even with unmeasured confounders.

Findings

Theorems establishing conditions for causal inference with latent variables

Conditions under which causal direction can be reliably identified

Framework for causal inference in the presence of unmeasured variables

Abstract

The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent variables makes it impossible to determine for two measured variables A and B, whether A causes B, B causes A, or there is some common cause. In this paper I present several theorems that state conditions under which it is possible to reliably infer the causal relation between two measured variables, regardless of whether latent variables are acting or not.