  Estimating an individual's potential response to interventions from
observational data is of high practical relevance for many domains, such as
healthcare, public policy or economics. In this setting, it is often the case
that combinations of interventions may be applied simultaneously, for example,
multiple prescriptions in healthcare or different fiscal and monetary measures
in economics. However, existing methods for counterfactual inference are
limited to settings in which actions are not used simultaneously. Here, we
present Neural Counterfactual Relation Estimation (NCoRE), a new method for
learning counterfactual representations in the combination treatment setting
that explicitly models cross-treatment interactions. NCoRE is based on a novel
branched conditional neural representation that includes learnt treatment
interaction modulators to infer the potential causal generative process
underlying the combination of multiple treatments. Our experiments show that
NCoRE significantly outperforms existing state-of-the-art methods for
counterfactual treatment effect estimation that do not account for the effects
of combining multiple treatments across several synthetic, semi-synthetic and
real-world benchmarks.
