The Inflation Technique for Causal Inference with Latent Variables
Elie Wolfe, Robert W. Spekkens, Tobias Fritz

TL;DR
The paper introduces the inflation technique, a novel method for causal inference with latent variables, enabling the derivation of new inequalities to test causal structure compatibility, including applications to quantum scenarios.
Contribution
It presents the inflation technique for causal inference, providing a systematic way to derive inequalities that detect incompatibility with causal models, even in quantum contexts.
Findings
Derived new inequalities for causal structures with latent variables.
The inequalities can detect incompatibility more effectively than existing methods.
Applicable to quantum and post-quantum causal models.
Abstract
The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the…
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Taxonomy
MethodsCausal inference
