Counterfactual causal analysis on structured data
- swarna paul ,
- Tauseef Jamal Firdausi ,
- Saikat Jana ,
- Arunava Das ,
- Piyush Nandi
Abstract
Data generated in a real-world business environment can be highly
connected with intricate relationships among entities. Studying
relationships and understanding their dynamics can provide deeper
understanding of business events. However, finding important causal
relations among entities is a daunting task with heavy dependency on
data scientists. Also due to fundamental problem of causal inference it
is impossible to directly observe causal effects. Thus, a method is
proposed to explain predictive causal relations in an arbitrary linked
dataset using counterfactual type causality. The proposed method can
generate counterfactual examples with high fidelity in minimal time. It
can explain causal relations among any chosen response variable and an
arbitrary set of independent causal variables to provide explanations in
natural language. The evidence of the explanations is shown in the form
of a summarized connected data graph