A graphical criterion for the identification of causal effects in linear models

This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is en-coded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. The paper establishes a suffi-cient criterion for the identifiability of all causal effects in such models as well as a procedure for estimating the causal effects from the observed covariance matrix