  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.
