New framework proposes 'computational identifiability' for practical ML

A new paper introduces the concept of "computational identifiability" as a practical alternative to theoretical identifiability in machine learning. This framework defines identifiability based on the success of a finite computational search procedure for an empirical estimator, rather than relying on idealized asymptotic conditions. The approach allows for answering fine-grained identification questions concerning small sample sizes, ambiguous graphical criteria, and mixed observational-interventional data. The authors provide experimental demonstrations and make their code available. AI

IMPACT Introduces a new framework for addressing practical identifiability challenges in machine learning models.

RANK_REASON The cluster contains a new academic paper introducing a novel concept in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• causal graph
• Computational Identifiability
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• estimator
• finite samples
• Hugging Face
• machine learning
• mixed observational-interventional data

• paper
• model release

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