  Inferring causal individual treatment effect (ITE) from observational data is
a challenging problem whose difficulty is exacerbated by the presence of
treatment assignment bias. In this work, we propose a new way to estimate the
ITE using the domain generalization framework of invariant risk minimization
(IRM). IRM uses data from multiple domains, learns predictors that do not
exploit spurious domain-dependent factors, and generalizes better to unseen
domains. We propose an IRM-based ITE estimator aimed at tackling treatment
assignment bias when there is little support overlap between the control group
and the treatment group. We accomplish this by creating diversity: given a
single dataset, we split the data into multiple domains artificially. These
diverse domains are then exploited by IRM to more effectively generalize
regression-based models to data regions that lack support overlap. We show
gains over classical regression approaches to ITE estimation in settings when
support mismatch is more pronounced.
