Derivation graphs characterize the space of do-calculus equivalent interventional expressions, enable identification with at most four rule applications, and yield multiple valid estimands for improved efficiency.
Abstracting causal models, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
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Presents a simple discrete primer on hierarchical causality that requires causation classes, aggregation operators, and discrete event-time maps to connect actor and agent levels.
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Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Derivation graphs characterize the space of do-calculus equivalent interventional expressions, enable identification with at most four rule applications, and yield multiple valid estimands for improved efficiency.
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A Simple Hierarchical Causality Primer
Presents a simple discrete primer on hierarchical causality that requires causation classes, aggregation operators, and discrete event-time maps to connect actor and agent levels.