AINeutralarXiv – CS AI · 8h ago5/10
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Optimal Experiments for Partial Causal Effect Identification
Researchers present a solution for selecting cost-effective experiments to narrow uncertainty bounds on partially identifiable causal effects from observational data. They formalize this as an NP-hard optimization problem and develop pruning algorithms that eliminate 50-88% of candidate experiments without exhaustive computation, demonstrated on real epidemiological datasets.