  Estimating the individuals' potential response to varying treatment doses is
crucial for decision-making in areas such as precision medicine and management
science. Most recent studies predict counterfactual outcomes by learning a
covariate representation that is independent of the treatment variable.
However, such independence constraints neglect much of the covariate
information that is useful for counterfactual prediction, especially when the
treatment variables are continuous. To tackle the above issue, in this paper,
we first theoretically demonstrate the importance of the balancing and
prognostic representations for unbiased estimation of the heterogeneous
dose-response curves, that is, the learned representations are constrained to
satisfy the conditional independence between the covariates and both of the
treatment variables and the potential responses. Based on this, we propose a
novel Contrastive balancing Representation learning Network using a partial
distance measure, called CRNet, for estimating the heterogeneous dose-response
curves without losing the continuity of treatments. Extensive experiments are
conducted on synthetic and real-world datasets demonstrating that our proposal
significantly outperforms previous methods.
