Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose
  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, generation of and heterogeneous causal effect
estimation. Although, there has been several association studies have been
proposed in the state-of-art methods, heterogeneous causal effects have never
been studied in concurrent drug usage and drug overdose problem. We apply our
framework to answer a critical question, "can concurrent usage of
benzodiazepines and opioids has heterogeneous causal effects on opioid overdose
epidemic?" Using Truven MarketScan claim data collected from 2001 to 2013 have
shown significant promise of our proposed framework's efficacy. Our efficient
causal inference model estimated that the causal effect is higher (19%) than
the regression studies (15%) to estimate the risks associated with the
concurrent usage of opioid and benzodiazepines on opioid overdose.
