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 example, in government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. The ultimate goal is to learn an optimal treatment policy that in some sense maximizes multiple outcomes simultaneously. To address such issues, we present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning with multiple objectives. We learn a low-dimensional representation of the true outcome from the observed outcomes using reduced rank regression. We develop a suite of estimates that use the model to denoise observed outcomes, including commonly-used index weightings. These methods improve estimation error in policy evaluation and optimization, including on a case study of real-world cash transfer and social intervention data. Reducing the variance of noisy social outcomes can improve the performance of algorithmic allocations.

翻译：评估潜在干预的因果效应对于指导决策至关重要，尤其是在改善机会获取方面。然而，若因果效应存在异质性且可通过协变量预测，个性化处理决策能够改善个体结果，并兼顾效率与公平性。实践中，因果研究人员往往并非预先设定单一结果变量，而是收集多个可能带有噪声的感兴趣结果作为真实目标的估计。例如，在政府资助的社会福利项目中，政策制定者通过收集多种结果来理解贫困的多维特征，其最终目标是学习一种能在某种意义上同时最大化多个结果的最优处理策略。为解决此类问题，我们提出了一种面向多目标最优策略学习的数据驱动降维方法。通过降秩回归从观测结果中学习真实结果的低维表征，并开发了一系列利用该模型对观测结果进行去噪的估计方法，包括常用的指数加权法。这些方法可降低策略评估与优化中的估计误差，并在实际现金转移与社会干预数据案例研究中得到验证。降低含噪社会结果的方差能有效提升算法分配的效能。