AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering...
This work evaluates methods for causal representation learning on a simple, real-world system that satisfies the basic problem setup of CRL, and finds that most methods already fail on this synthetic ablation despite its simple data-generating process.
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