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.

本文研究了从记录的上下文、决策和结果中估计单个患者对替代药物的反应的个体层面因果效应的估计，并给出了基于不同治疗组之间的距离度量的误差的概括界限，引导了表示学习算法的开发，该算法通过规范化表示的诱导治疗组距离，鼓励治疗组之间信息的共享来最小化误差的界限。最后，对真实和合成数据的实验评估表明了所提出的表示架构和规范化方案的价值。