Overcoming the Computing Barriers in Statistical Causal Inference

The massive development in statistical causal inference to the era of big data commonly seen in public health applications can be always hindered due to the computational barriers. In this chapter we discuss a practical concern on computing barriers in statistical causal inference with example in optimal pair matching and consequently offer a novel solution by constructing a stratification tree based on exact matching and propensity scores. We demonstrate the implementation of this novel method with a large observational study from Philadelphia obstetric unit closure from 1995 to 2003 with 59 observed covariates in each of the 132,786 birth deliveries and 5,998,111 potential controls. Algorithms and R program code are also provided for interested readers.