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.
Proceedings of the AAAI Conference on Artificial Intelligence , author=
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Authors create a benchmark across discrete/continuous and static/dynamical systems and introduce the Causal Abstraction Error (CAE) metric that reliably distinguishes valid from invalid causal abstractions when it includes faithfulness testing.
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.
citing papers explorer
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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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Validating Causal Abstraction Metrics on Simulated Complex Systems
Authors create a benchmark across discrete/continuous and static/dynamical systems and introduce the Causal Abstraction Error (CAE) metric that reliably distinguishes valid from invalid causal abstractions when it includes faithfulness testing.
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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.