Radical empiricism and machine learning research

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

Journal of Causal Inference, 2021, vol. 9, issue 1, 78-82

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.

Keywords: causal models; knowledge representation; machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:9:y:2021:i:1:p:78-82:n:2

DOI: 10.1515/jci-2021-0006

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