mlsanityData to 14 Oct 2026

Causal Inference for Banking Finance and Insurance A Survey

arXiv 2307.16427 ↗First posted 31 Jul 2023cs.AI cs.LG econ.EMcited by 5

Not on the map. Why?

Abstract (arXiv)

Causal Inference plays an significant role in explaining the decisions taken by statistical models and artificial intelligence models. Of late, this field started attracting the attention of researchers and practitioners alike. This paper presents a comprehensive survey of 37 papers published during 1992-2023 and concerning the application of causal inference to banking, finance, and insurance. The papers are categorized according to the following families of domains: (i) Banking, (ii) Finance and its subdomains such as corporate finance, governance finance including financial risk and financial policy, financial economics, and Behavioral finance, and (iii) Insurance. Further, the paper covers the primary ingredients of causal inference namely, statistical methods such as Bayesian Causal Network, Granger Causality and jargon used thereof such as counterfactuals. The review also recommends some important directions for future research. In conclusion, we observed that the application of causal inference in the banking and insurance sectors is still in its infancy, and thus more research is possible to turn it into a viable method.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 81 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

15 of 81 references.

References · 15

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  2. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 95
  3. 2021A survey of Bayesian Network structure learningcited by 87
  4. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  5. 2021Granger Causality: A Review and Recent Advancescited by 63
  6. 2018Interpretable Credit Application Predictions With Counterfactual Explanationscited by 43
  7. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  8. 2020Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classificationcited by 40
  9. 2020Causality Learning: A New Perspective for Interpretable Machine Learningcited by 28
  10. 2014Causal Inference by Identification of Vector Autoregressive Processes with Hidden Componentscited by 11
  11. 2019Learning Interpretable Models with Causal Guaranteescited by 11
  12. 2022Counterfactual Explanations Using Optimization With Constraint Learningcited by 8
3 more
  1. 2023Causal Analysis of Customer Churn Using Deep Learningcited by 3
  2. 2016Non-Parametric Causality Detection: An Application to Social Media and Financial Datacited by 2
  3. 2017Causal Data Science for Financial Stress Testingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Bridging Language Models and Financial Analysiscited by 4
  2. 2025ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencodercited by 1
  3. 2025ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AIcited by 1
  4. 2025The Estimation of Continual Causal Effect for Dataset Shifting Streamscited by 0
  5. 2026An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Makingcited by 0