Computational Identifiability (arxiv.org)

A recent study introduced the concept of "computational identifiability," distinguishing it from traditional notions of theoretical identifiability in causal inference. This new framework emphasizes the practical aspects of identifying causal effects using finite computational procedures, rather than relying on idealized conditions such as infinite data. By focusing on the empirical estimation of causal effects within specified error tolerances, researchers can address complex identification scenarios, including those involving small sample sizes and mixed observational-interventional data. This advancement is significant for the AI and machine learning community as it provides a more accessible and applicable method for causal inference. The proposed computational identifiability framework allows practitioners to derive practical answers to identification questions that arise in various real-world applications. By making causal analysis more computationally feasible, this work opens the door for improved decision-making in fields ranging from economics to healthcare, where understanding causal relationships is essential. The accompanying code is made available, promoting further exploration and implementation of these concepts in empirical research.