A Graphical Criterion for the Identification of Causal Effects in Linear Models

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 18

Probabilistic and Causal Reasoning

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 encoded 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 sufficient 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.