Testable Implications of Linear Structural Equation Models

  title={Testable Implications of Linear Structural Equation Models},
  author={Bryant Chen and Jin Tian and Judea Pearl},
In causal inference, all methods of model learning rely on testable implications, namely, properties of the joint distribution that are dictated by the model structure. These constraints, if not satisfied in the data, allow us to reject or modify the model. Most common methods of testing a linear structural equation model (SEM) rely on the likelihood ratio or chi-square test which simultaneously tests all of the restrictions implied by the model. Local constraints, on the other hand, offer… CONTINUE READING
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