  Feature selection is an important problem in machine learning, which aims to
select variables that lead to an optimal predictive model. In this paper, we
focus on feature selection for post-intervention outcome prediction from
pre-intervention variables. We are motivated by healthcare settings, where the
goal is often to select the treatment that will maximize a specific patient's
outcome; however, we often do not have sufficient randomized control trial data
to identify well the conditional treatment effect. We show how we can use
observational data to improve feature selection and effect estimation in two
cases: (a) using observational data when we know the causal graph, and (b) when
we do not know the causal graph but have observational and limited experimental
data. Our paper extends the notion of Markov boundary to treatment-outcome
pairs. We provide theoretical guarantees for the methods we introduce. In
simulated data, we show that combining observational and experimental data
improves feature selection and effect estimation.
