Identification Invariance
A core concept in causal inference describing when a clustering operation is safe, defined as the property that the operation simultaneously preserves both the identifiability and non-identifiability conclusions of the original causal graph. It serves as a mathematical criterion for determining whether graph simplification introduces bias.
Timeline (last 90 days)
The paper proposes the concept of identification invariance: a clustering operation is called identification-invariant when it simultaneously preserves the conclusions in both directions—identifiability and non-identifiability.
The main contribution of the paper is to identify a large class of clustering operations that satisfy identification invariance, and to provide a criterion centered on the c-components (confounded components) of the original graph.
The core criterion of the paper is that a safe clustering operation must respect the topological structure of the c-components of the original graph and must not arbitrarily merge variables belonging to different c-components into the same cluster node.
The paper demonstrates through practical scenarios that identification-invariant clustering can be applied in real causal inference tasks, and is applicable to complex variable network domains such as epidemiology, economics policy evaluation, and A/B experiment analysis.
All Facts (4)
The paper proposes the concept of identification invariance: a clustering operation is called identification-invariant when it simultaneously preserves the conclusions in both directions—identifiability and non-identifiability.
50%UnverifiedThe main contribution of the paper is to identify a large class of clustering operations that satisfy identification invariance, and to provide a criterion centered on the c-components (confounded components) of the original graph.
50%UnverifiedThe core criterion of the paper is that a safe clustering operation must respect the topological structure of the c-components of the original graph and must not arbitrarily merge variables belonging to different c-components into the same cluster node.
50%UnverifiedThe paper demonstrates through practical scenarios that identification-invariant clustering can be applied in real causal inference tasks, and is applicable to complex variable network domains such as epidemiology, economics policy evaluation, and A/B experiment analysis.
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