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Radical empiricism and machine learning research

Author

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  • Pearl Judea

    (University of California, Los Angeles, Computer Science Department, Los Angeles, CA, 90095-1596, United States of America)

Abstract

I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.

Suggested Citation

  • Pearl Judea, 2021. "Radical empiricism and machine learning research," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 78-82, January.
    • Handle: RePEc:bpj:causin:v:9:y:2021:i:1:p:78-82:n:2
    DOI: 10.1515/jci-2021-0006
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