Toward Accounting for Hidden Common Causes When Inferring Cause and Effect from Observational Data

Hidden common causes make it difficult to infer causal relationships from observational data. Here, we begin an investigation into a new method to account for a hidden common cause that infers its presence from the data. As with other approaches that can account for common causes, this approach is successful only in some cases. We describe such a case taken from the field of genomics, wherein one tries to identify which genomic markers causally influence a trait of interest.