We investigate how to exploit structural similarities of an individual's
potential outcomes (POs) under different treatments to obtain better estimates
of conditional average treatment effects in finite samples. Especially when it
is unknown whether a treatment has an effect at all, it is natural to
hypothesize that the POs are similar - yet, some existing strategies for
treatment effect estimation employ regularization schemes that implicitly
encourage heterogeneity even when it does not exist and fail to fully make use
of shared structure. In this paper, we investigate and compare three end-to-end
learning strategies to overcome this problem - based on regularization,
reparametrization and a flexible multi-task architecture - each encoding
inductive bias favoring shared behavior across POs. To build understanding of
their relative strengths, we implement all strategies using neural networks and
conduct a wide range of semi-synthetic experiments. We observe that all three
approaches can lead to substantial improvements upon numerous baselines and
gain insight into performance differences across various experimental settings.

研究结构化相似性如何对待不同治疗方式下潜在结果进行利用，以获得有限样本中条件平均治疗效果的更好估计，比较三种端到端学习策略以解决存在异质性和利用共享结构不充分问题，实验结果表明三种方法都能对许多基准产生实质性提升，并获得了不同实验设置下性能差异的深入认识。