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  • April–June 2022
  • Other Article
  • INFORMS Journal on Data Science

Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'

By: Edward McFowland III
  • Format:Print
  • | Pages:2
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Abstract

There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision allocations. I commend Fernández-Loría and Provost (2021) for highlighting the important, and in retrospect, intuitive differences between causal estimation and causal decision making. This commentary is aimed at expanding the conversation in fruitful directions, with an eye toward real-world practice in organizations. Specifically, I highlight that future work will need to address how to exploit these theoretical results in practice (Section 2), how to ensure that causal decisions are fair (Section 3), and what are the additional benefits and challenges when there is a human in the loop (Section 4).

Keywords

Causal Inference; Treatment Effect Estimation; Treatment Assignment Policy; Human-in-the-loop; Decision Making; Fairness

Citation

McFowland III, Edward. "Commentary on '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): 21–22.
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About The Author

Edward McFowland III

Technology and Operations Management
→More Publications

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    • 2023
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    Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

    By: Frabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
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    So, Who Likes You? Evidence from a Randomized Field Experiment

    By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
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    Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

    By: Neil Menghani, Edward McFowland III and Daniel B. Neill
More from the Author
  • Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality By: Frabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
  • So, Who Likes You? Evidence from a Randomized Field Experiment By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
  • Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness By: Neil Menghani, Edward McFowland III and Daniel B. Neill
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