Learning continuous-valued treatment effects through representation
  balancing
Christopher Bockel-Rickermann Toon Vanderschueren Jeroen Berrevoets Tim Verdonck Wouter Verbeke
Estimating the effects of treatments with an associated dose on an instance's
outcome, the "dose response", is relevant in a variety of domains, from
healthcare to business, economics, and beyond. Such effects, also known as
continuous-valued treatment effects, are typically estimated from observational
data, which may be subject to dose selection bias. This means that the
allocation of doses depends on pre-treatment covariates. Previous studies have
shown that conventional machine learning approaches fail to learn accurate
individual estimates of dose responses under the presence of dose selection
bias. In this work, we propose CBRNet, a causal machine learning approach to
estimate an individual dose response from observational data. CBRNet adopts the
Neyman-Rubin potential outcome framework and extends the concept of balanced
representation learning for overcoming selection bias to continuous-valued
treatments. Our work is the first to apply representation balancing in a
continuous-valued treatment setting. We evaluate our method on a newly proposed
benchmark. Our experiments demonstrate CBRNet's ability to accurately learn
treatment effects under selection bias and competitive performance with respect
to other state-of-the-art methods.