Why expect causation at all? A pessimistic parallel with neuroscience


In their target article, Lynch, Parke, and O’Malley argue against the quick application of causal, interventionist explanatory frameworks to microbiomes and their purported role in many disparate states, from obesity to anxiety. I think the authors have undersold the force of their argument. A careful consideration of the scope of their claims, made easier by a parallel drawn from the history of explanation in neuroscience, yields a productive pessimism: that causal explanations likely operate at the wrong level of analysis for dynamic, distributed, Quineian entities like the microbiome. That is, we shouldn’t expect causal explanations for microbiomes at all—and this includes the authors’ own “microbiome success story” of C. difficile. Neuroscience, with its own computationally challenging, dynamic entity—the brain—may provide lessons for how to approach something like predictive control over the microbiome.

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    Enterotypes might turn out to be a twenty-first century star-chart, with many internet services helping you “find your enterotype” and matching you to a probiotic blend that they conveniently stock.

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    For instance, you could train a decoding model to predict, given a pattern of neural activation, which stimulus was most likely present. Likewise, you could train an encoding model to do the opposite. Applying statistical learning techniques, including Bayesian strategies, can further the predictive power of these models, as Schoenmakers et al. (2013) demonstrate.


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Correspondence to Javier Gomez-Lavin.

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Gomez-Lavin, J. Why expect causation at all? A pessimistic parallel with neuroscience. Biol Philos 34, 61 (2019). https://doi.org/10.1007/s10539-019-9713-z

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  • Microbiomes
  • Clostridium difficile
  • Quineian
  • Neuroimaging
  • Explanation in neuroscience
  • Multivariate analysis