Causal Machine Learning - New Opportunities for Information Systems Research

Authors

Domenic Kellner, Goethe University FrankfurtFollow

Paper Number

1383

Abstract

Machine Learning (ML) is increasingly being adopted in Information Systems (IS) research and organizational decision-making. Especially, understanding the causal mechanism between input and outcome variables and going beyond prediction-based and primarily correlational ML techniques becomes key to decision making. By conducting a scoping review and searching relevant literature databases with appropriate search terms within Causal Machine Learning (CML) scope, we select 36 papers practical for IS research and practitioners alike. By analyzing the several research articles, our study contributes seven fascinating areas of interest, namely (1) Causality and Explainable AI, (2) Causality and Algorithmic Fairness, (3) Causal Effect Estimation, (4) Causality and Decision Making, (5) Causality and Natural Language Processing, (6) Causal Domain Adaption, and (7) Causal Reinforcement Learning. Moreover, our study proposes three exciting avenues of research for IS scholars and highlights the crucial role of CML, therefore, offering a new perspective on data-augmented decision-making.

Comments

Paper Number 1383

Recommended Citation

Kellner, Domenic, "Causal Machine Learning - New Opportunities for Information Systems Research" (2022). PACIS 2022 Proceedings. 181.
https://aisel.aisnet.org/pacis2022/181