Aadhaar Inclusion and Causality Inference

According to a report published today in Economic Times, 92% of the population in India (based on 2018 consensus) with nearly all adults barring 3.5 million have an Aadhaar card. This is a phenomenal achievement in bringing underprivileged people for social and financial inclusion. Aadhaar card’s unique identification number will soon work as a social security number for the population. Making aadhaar card essential for all monetary dealings ultimately aids in widening tax bracket. Over a period of time, casual inference using multivariate analysis will help in measuring the social impact of aadhaar inclusion.

Coming to multivariate analysis which is correlation analysis using multiple variables to measure causality. Although Random Control Trial (RCT) is the gold standard to measure causality between two randomly selected samples sizes i.e. control group and treatment group,  in this case, it will not be possible to make two groups randomly selected i.e. two groups with very similar demographic profile randomly selected, one sample without aadhaar card and one sample with aadhaar card. However, a time series data of the population over a decade before and after aadhaar implementation might lead to useful insights. This form of statistical analysis is not a form of RCT analysis.  Continue reading “Aadhaar Inclusion and Causality Inference”