Detecting Causal Relations in the Presence of Unmeasured Variables
This addresses a fundamental challenge in causal inference for researchers and practitioners dealing with complex data, though it appears incremental as it builds on existing theoretical frameworks.
The paper tackles the problem of inferring causal relations between measured variables when latent variables are present, presenting theorems that specify conditions under which reliable causal inference is possible regardless of latent 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.