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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:Electronic
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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).
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About The Author

Edward McFowland III

Technology and Operations Management
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More from the Author

    • October–December 2022
    • INFORMS Journal on Data Science

    Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

    By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
    • 2022
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    Nonparametric Subset Scanning for Detection of Heteroscedasticity

    By: Charles R. Doss and Edward McFowland III
    • Pattern Recognition Letters

    Pattern Detection in the Activation Space for Identifying Synthesized Content

    By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam
More from the Author
  • Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
  • Nonparametric Subset Scanning for Detection of Heteroscedasticity By: Charles R. Doss and Edward McFowland III
  • Pattern Detection in the Activation Space for Identifying Synthesized Content By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam
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