Learning representations for counterfactual inference from observational data
is of high practical relevance for many domains, such as healthcare, public
policy and economics. Counterfactual inference enables one to answer "What
if...?" questions, such as "What would be the outcome if we gave this patient
treatment $t_1$?". However, current methods for training neural networks for
counterfactual inference on observational data are either overly complex,
limited to settings with only two available treatments, or both. Here, we
present Perfect Match (PM), a method for training neural networks for
counterfactual inference that is easy to implement, compatible with any
architecture, does not add computational complexity or hyperparameters, and
extends to any number of treatments. PM is based on the idea of augmenting
samples within a minibatch with their propensity-matched nearest neighbours.
Our experiments demonstrate that PM outperforms a number of more complex
state-of-the-art methods in inferring counterfactual outcomes across several
benchmarks, particularly in settings with many treatments.

本研究提出了一种名为 Perfect Match 的方法，它基于近邻匹配来训练神经网络进行反事实推断，可用于处理多种治疗方案，具有易实现、不增加计算复杂度等特点，并在多个基准测试中显示出优异的性能。