  Performing inference on data obtained through observational studies is
becoming extremely relevant due to the widespread availability of data in
fields such as healthcare, education, retail, etc. Furthermore, this data is
accrued from multiple homogeneous subgroups of a heterogeneous population, and
hence, generalizing the inference mechanism over such data is essential. We
propose the MetaCI framework with the goal of answering counterfactual
questions in the context of causal inference (CI), where the factual
observations are obtained from several homogeneous subgroups. While the CI
network is designed to generalize from factual to counterfactual distribution
in order to tackle covariate shift, MetaCI employs the meta-learning paradigm
to tackle the shift in data distributions between training and test phase due
to the presence of heterogeneity in the population, and due to drifts in the
target distribution, also known as concept shift. We benchmark the performance
of the MetaCI algorithm using the mean absolute percentage error over the
average treatment effect as the metric, and demonstrate that meta
initialization has significant gains compared to randomly initialized networks,
and other methods.

Machine Learning;Multiagent Systems;Machine Learning