1. S. Feuerriegel, Y.R. Shrestha, G. von Krogh, et al., “Bringing Artificial Intelligence to Business Management,” Nature Machine Intelligence 4, no. 7 (July 2022): 611-613; and P. Hünermund, J. Kaminski, and C. Schmitt, “Causal Machine Learning and Business Decision-Making,” SSRN, updated Feb. 19, 2022, https://ssrn.com.
2. S. Feuerriegel, D. Frauen, V. Melnychuk, et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30 (April 2024): 958-968; V. Chernozhukov, C. Hansen, N. Kallus, et al., “Applied Causal Inference Powered by ML and AI,” PDF file (pub. by the authors, July, 28, 2024), https:causalml-book.org; and C. Fernández-Loría and F. Provost, “Causal Decision-Making and Causal Effect Estimation Are Not the Same … and Why It Matters,” Informs Journal on Data Science 1, no. 1 (April-June 2022): 4-16.
3. M. von Zahn, K. Bauer, C. Mihale-Wilson, et al., “Smart Green Nudging: Reducing Product Returns Through Digital Footprints and Causal Machine Learning,” Marketing Science, Articles in Advance, published online Aug. 8, 2024; E. Ascarza, “Retention Futility: Targeting High-Risk Customers Might Be Ineffective,” Journal of Marketing Research 55, no. 1 (February 2018): 80-98; J. Yang, D. Eckles, P. Dhillon, et al., “Targeting for Long-Term Outcomes,” Management Science 70, no. 6 (June 2024): 3841-3855; and M. Kraus, S. Feuerriegel, and M. Saar-Tsechansky, “Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II,” Manufacturing & Service Operations Management 26, no. 1 (January-February 2024): 137-153.
4. G. von Krogh, S.M. Ben-Menahem, and Y.R. Shrestha, “Artificial Intelligence in Strategizing: Prospects and Challenges,” in “Strategic Management: State of the Field and Its Future,” eds. I.M. Duhaime, M.A. Hitt, and M.A. Lyles. (New York: Oxford University Press, 2021), 625-646.
5. “Premium Chocolate Production Perfected: AI’s Role in Quality Excellence,” ETH AI Center, Dec. 11, 2023, https://ai.ethz.ch.
6. J. Senoner, T. Netland, and S. Feuerriegel, “Using Explainable Artificial Intelligence to Improve Process Quality: Evidence From Semiconductor Manufacturing,” Management Science 68, no. 8 (August 2022): 5704-5723.
7. H. Wasserbacher and M. Spindler, “Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls,” Digital Finance 4 (March 2022): 63-88.
8. J. Persson, S. Feuerriegel, and C. Kadar, “Off-Policy Learning for Audience-Wide Content Promotions,” working paper, 2023.
9. Ibid.
10. Senoner et al., “Using Explainable Artificial Intelligence,” 5704-5723.
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