Determinantal generalizations of instrumental variables
L Weihs, B Robinson, E Dufresne, J Kenkel… - Journal of Causal …, 2018 - degruyter.com
Linear structural equation models relate the components of a random vector using linear
interdependencies and Gaussian noise. Each such model can be naturally associated with
a mixed graph whose vertices correspond to the components of the random vector. The
graph contains directed edges that represent the linear relationships between components,
and bidirected edges that encode unobserved confounding. We study the problem of
generic identifiability, that is, whether a generic choice of linear and confounding effects can …
interdependencies and Gaussian noise. Each such model can be naturally associated with
a mixed graph whose vertices correspond to the components of the random vector. The
graph contains directed edges that represent the linear relationships between components,
and bidirected edges that encode unobserved confounding. We study the problem of
generic identifiability, that is, whether a generic choice of linear and confounding effects can …
Determinantal generalizations of instrumental variables
W Luca, R Bill, D Emilie, K Jennifer… - Journal of Causal …, 2018 - econpapers.repec.org
Linear structural equation models relate the components of a random vector using linear
interdependencies and Gaussian noise. Each such model can be naturally associated with
a mixed graph whose vertices correspond to the components of the random vector. The
graph contains directed edges that represent the linear relationships between components,
and bidirected edges that encode unobserved confounding. We study the problem of
generic identifiability, that is, whether a generic choice of linear and confounding effects can …
interdependencies and Gaussian noise. Each such model can be naturally associated with
a mixed graph whose vertices correspond to the components of the random vector. The
graph contains directed edges that represent the linear relationships between components,
and bidirected edges that encode unobserved confounding. We study the problem of
generic identifiability, that is, whether a generic choice of linear and confounding effects can …
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