Abstract
Under current policy decision making paradigm, we make or evaluate a policy
decision by intervening different socio-economic parameters and analyzing the
impact of those interventions. This process involves identifying the causal
relation between interventions and outcomes. Matching method is one of the
popular techniques to identify such causal relations. However, in one-to-one
matching, when a treatment or control unit has multiple pair assignment options
with similar match quality, different matching algorithms often assign
different pairs. Since, all the matching algorithms assign pair without
considering the outcomes, it is possible that with same data and same
hypothesis, different experimenters can make different conclusions. This
problem becomes more prominent in case of large-scale observational studies.
Recently, a robust approach is proposed to tackle the uncertainty which uses
discrete optimization techniques to explore all possible assignments. Though
optimization techniques are very efficient in its own way, they are not
scalable to big data. In this work, we consider causal inference testing with
binary outcomes and propose computationally efficient algorithms that are
scalable to large-scale observational studies. By leveraging the structure of
the optimization model, we propose a robustness condition which further reduces
the computational burden. We validate the efficiency of the proposed algorithms
by testing the causal relation between Hospital Readmission Reduction Program
(HRRP) and readmission to different hospital (non-index readmission) on the
State of California Patient Discharge Database from 2010 to 2014. Our result
shows that HRRP has a causal relation with the increase in non-index
readmission and the proposed algorithms proved to be highly scalable in testing
causal relations from large-scale observational studies.
Description
[1904.02190] Robust policy evaluation from large-scale observational studies
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