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 approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available%2C to improve performance. In addition to being more time and data-efficient%2C the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships%2C showcasing its potential for broad applicability in causal graph discovery tasks across different domains.?quality=80&w=800)
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