Detecting Causal Relations in the Presence of Unmeasured Variables

📝 Original Info

• Title: Detecting Causal Relations in the Presence of Unmeasured Variables
• ArXiv ID: 1303.5754
• Date: 2013-03-26
• Authors: Researchers from original ArXiv paper

📝 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.

💡 Deep Analysis

Deep Dive into Detecting Causal Relations in the Presence of Unmeasured Variables.

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.

📄 Full Content

Reference