Decomposition and Identification of Linear Structural Equation Models
This work addresses identification challenges in causal inference for researchers, offering incremental improvements in methodology.
The paper tackles the problem of identifying linear structural equation models by extending the edge set half-trek criterion to cover a broader class and recursively decomposing semi-Markovian linear models into simpler sub-models, resulting in improved identification power that subsumes non-parametric model algorithms.
In this paper, we address the problem of identifying linear structural equation models. We first extend the edge set half-trek criterion to cover a broader class of models. We then show that any semi-Markovian linear model can be recursively decomposed into simpler sub-models, resulting in improved identification power. Finally, we show that, unlike the existing methods developed for linear models, the resulting method subsumes the identification algorithm of non-parametric models.