Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
Pearl's Calculus of Intervention Is Complete
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper is concerned with graphical criteria that can be used to solve the problem of identifying casual effects from nonexperimental data in a causal Bayesian network structure, i.e., a directed acyclic graph that represents causal relationships. We first review Pearl's work on this topic [Pearl, 1995], in which several useful graphical criteria are presented. Then we present a complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. By exploiting the completeness of this algorithm, we prove that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-calculus that transforms the causal effect formula into a formula that only includes observational quantities.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes possibility space, timing computation, and causal factum as a new framework for data-driven trajectory discovery and counterfactual timing deduction on EHR data from 3,276 breast cancer patients.
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Identifying Interventional Joint Distributions via Extended Bridge Functions
Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
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To Use AI as Dice of Possibilities with Timing Computation
Proposes possibility space, timing computation, and causal factum as a new framework for data-driven trajectory discovery and counterfactual timing deduction on EHR data from 3,276 breast cancer patients.