Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performing experiments and a recent monumental increase in electronic record keeping has brought attention to the problem of evaluating decisions based on non-experimental observational data. This is the setting of this work. In particular, we study estimation of individual-level causal effects, such as a single patient's response to alternative medication, from recorded contexts, decisions and outcomes. We give generalization bounds on the error in estimated effects based on distance measures between groups receiving different treatments, allowing for sample re-weighting. We provide conditions under which our bound is tight and show how it relates to results for unsupervised domain adaptation. Led by our theoretical results, we devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance, and encourage sharing of information between treatment groups. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances. Finally, an experimental evaluation on real and synthetic data shows the value of our proposed representation architecture and regularization scheme.

翻译：医疗、经济和教育等不同领域的从业者迫切希望应用机器学习来改进决策制定。由于开展实验的成本高昂且不切实际，加之近年来电子记录保存量的惊人增长，基于非实验性观测数据评估决策的问题日益受到关注。这正是本工作的研究背景。具体而言，我们研究了如何根据记录的情境、决策和结果，估计个体层面的因果效应（例如单个患者对替代药物的反应）。我们基于接受不同处理组之间的距离度量，给出了估计效应误差的泛化界，并允许进行样本重新加权。我们提供了该界限紧致的条件，并展示了其与无监督领域自适应结果之间的关联。受理论结果的启发，我们设计了表示学习算法，通过正则化表示所诱导的处理组距离来最小化我们的界限，并鼓励处理组之间的信息共享。我们将这些算法扩展为同时学习加权表示，以进一步减少处理组距离。最后，在真实和合成数据上的实验评估表明了所提出的表示架构与正则化方案的价值。