Identifying Direct Causal Effects in Linear Models

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Automated Reasoning

This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.