<ul><li>Policy evaluation and learning often assume no interference among units, but this can lead to biased evaluation and learning outcomes.</li><li>The paper focuses on individualized treatment rules (ITR) under clustered network interference.</li><li>A semiparametric structural model is used to evaluate the performance of ITR.</li><li>The proposed methodology improves the performance of learned policies through more efficient evaluation.</li></ul>

Individualized Policy Evaluation and Learning under Clustered Network Interference

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