Causal inference — what the machine cannot learn

12 Sep, 2022 at 16:55 | Posted in Statistics & Econometrics | 3 Comments

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The central problem with the present ‘machine learning’ and ‘big data’ hype is that so many — falsely — think that they can get away with analyzing real-world phenomena without any (commitment to) theory. But — data never speaks for itself. Without a prior statistical set-up, there actually are no data at all to process.

Clever data-mining tricks are not enough to answer important scientific questions. Theory matters.

maIf we wanted highly probable claims, scientists would stick to​​ low-level observables and not seek generalizations, much less theories with high explanatory content. In this day​ of fascination with Big data’s ability to predict​ what book I’ll buy next, a healthy Popperian reminder is due: humans also want to understand and to explain. We want bold ‘improbable’ theories. I’m a little puzzled when I hear leading machine learners praise Popper, a realist, while proclaiming themselves fervid instrumentalists. That is, they hold the view that theories, rather than aiming at truth, are just instruments for organizing and predicting observable facts. It follows from the success of machine learning, Vladimir Cherkassy avers, that​ “realism is not possible.” This is very quick philosophy!

Quick indeed!

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  1. “We all start from naive realism, i.e., the doctrine that
    things are what they seem. We think that grass is green,
    that stones are hard, and that snow is cold. But physics
    assures us that the greenness of grass, the hardness of
    stones, and coldness of snow are not the greenness, hardness,
    and coldness that we know in our experience, but something
    very different.

    The observer, when he seems to himself to be observing a stone,
    is really, if physics is to be believed, observing the effects of the
    stone upon himself. Thus, science seems to be at war upon itself.

    When it most means to be objective, it finds itself plunged
    into subjectivity against its will. Naive realism leads to physics;
    and physics, if true, shows that naive realism is false.
    Therefore, naive realism, if true, is false; therefore, it’s false.

    And therefore the behaviourist, when he thinks he is recording observations about the outer world, is really recording observations about what is happening in him.”

    -Bertrand Russell, An Inquiry into Meaning and Truth (1940), Introduction, p. 15.

  2. Now matter the expertise in the use of machine to manipulate big data. Without clear knowledge of economic theories, it will be gibberish.

  3. An interesting couple of links.
    Provoked rather a lot of thoughts from my own research.

    What the Machine can not learn , Lars Syll, #DataGoy

    Praxeology
    Austrians argue that empirical data itself is insufficient to describe economics; that consequently, empirical data cannot falsify economic theory; that logical positivism cannot predict or explain human action; and that the methodological requirements of logical positivism are impossible to obtain for economic questions.[18][12] Ludwig von Mises in particular argued against empiricist approaches to the social sciences in general, because human events are unique and non-repeatable, whereas experiments in the physical sciences are necessarily reproducible.[18]

    Two dogmas of empiricism.
    ´´As an empiricist, I continue to think of the conceptual scheme of science as a tool, ultimately, for predicting future experience in the light of past experience. Physical objects are conceptually imported into the situation as convenient intermediaries — not by definition in terms of experience, but simply as irreducible posits18b comparable, epistemologically, to the gods of Homer. Let me interject that for my part I do, qua lay physicist, believe in physical objects and not in Homer’s gods; and I consider it a scientific error to believe otherwise.´´

    Wittgenstein put it rather well and he was not intentionally mendacious.

    “Remember that we sometimes demand explanations for the sake not of their content, but of their form. Our requirement is an architectural one; the explanation is a kind of sham corbel that supports nothing.”
    ― Ludwig Wittgenstein, Philosophical Investigations

    “All theory is gray, my friend. But forever green is the tree of life.”

    ― Johann Wolfgang von Goethe, Faust, First Part


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