  A Randomized Control Trial (RCT) is considered as the gold standard for
evaluating the effect of any intervention or treatment. However, its
feasibility is often hindered by ethical, economical, and legal considerations,
making observational data a valuable alternative for drawing causal
conclusions. Nevertheless, healthcare observational data presents a difficult
challenge due to its high dimensionality, requiring careful consideration to
ensure unbiased, reliable, and robust causal inferences. To overcome this
challenge, in this study, we propose a novel two-stage feature selection
technique called, Outcome Adaptive Elastic Net (OAENet), explicitly designed
for making robust causal inference decisions using matching techniques. OAENet
offers several key advantages over existing methods: superior performance on
correlated and high-dimensional data compared to the existing methods and the
ability to select specific sets of variables (including confounders and
variables associated only with the outcome). This ensures robustness and
facilitates an unbiased estimate of the causal effect. Numerical experiments on
simulated data demonstrate that OAENet significantly outperforms
state-of-the-art methods by either producing a higher-quality estimate or a
comparable estimate in significantly less time. To illustrate the applicability
of OAENet, we employ large-scale US healthcare data to estimate the effect of
Opioid Use Disorder (OUD) on suicidal behavior. When compared to competing
methods, OAENet closely aligns with existing literature on the relationship
between OUD and suicidal behavior. Performance on both simulated and real-world
data highlights that OAENet notably enhances the accuracy of estimating
treatment effects or evaluating policy decision-making with causal inference.
