A Review of Causal Identifiability Techniques across Different Observational Datasets
We present an aggregation of the causal identifiability solutions techniques and their assumptions as advanced in extant literatures with datasets of odd origins, which do not necessarily conform to the independent and identically distributed (i.i.d) dataset, multinomial datasets and the Gaussian datasets settings; alongside their concomitant assumptions. The transformation process in data generation can sometimes […]
