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Reduced-Rank Multi-objective Policy Learning and Optimization
April 30, 2024, 4:42 a.m. | Ezinne Nwankwo, Michael I. Jordan, Angela Zhou
cs.LG updates on arXiv.org arxiv.org
Abstract: Evaluating the causal impacts of possible interventions is crucial for informing decision-making, especially towards improving access to opportunity. However, if causal effects are heterogeneous and predictable from covariates, personalized treatment decisions can improve individual outcomes and contribute to both efficiency and equity. In practice, however, causal researchers do not have a single outcome in mind a priori and often collect multiple outcomes of interest that are noisy estimates of the true target of interest. For …
abstract access arxiv causal cs.lg decision decisions effects efficiency equity however impacts improving making multi-objective optimization personalized policy practice researchers stat.ml treatment type
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