  Drug overdose has become a public health crisis in the United States with
devastating consequences. However, most of the drug overdose incidences are the
consequence of recitative polysubstance usage over a defined period of time
which can be happened by either the intentional usage of required drug with
other drugs or by accident. Thus, predicting the effects of polysubstance usage
is extremely important for clinicians to decide which combination of drugs
should be prescribed. Recent advancement of structural causal models can
provide ample insights of causal effects from observational data via
identifiable causal directed graphs. In this paper, we propose a system to
estimate heterogeneous concurrent drug usage effects on overdose estimation,
that consists of efficient co-variate selection, sub-group selection and
heterogeneous causal effect estimation. We apply our framework to answer a
critical question, can concurrent usage of benzodiazepines and opioids have
heterogeneous causal effects on the opioid overdose epidemic? Using Truven
MarketScan claim data collected from 2001 to 2013 have shown significant
promise of our proposed framework's efficacy.
