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Counterfactual causal analysis on structured data

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posted on 25.05.2021, 02:35 by swarna paulswarna paul, Tauseef Jamal Firdausi, Saikat Jana, Arunava Das, Piyush Nandi
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

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Email Address of Submitting Author

swarna.kpaul@gmail.com

ORCID of Submitting Author

0000-0002-2362-935X

Submitting Author's Institution

Tata Consultancy Services

Submitting Author's Country

India

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