Skip to main content
SpringerLink
Log in

Disentangling Mechanisms from Causes: And the Effects on Science

  • Published:
Foundations of Science Aims and scope Submit manuscript

Abstract

Despite the miraculous progress of science—it’s practitioners continue to run into mistakes, either discrediting research unduly or making leaps of causal inference where none are warranted. In this we isolate two of the reasons for such behavior involving the misplaced understanding of the role of mechanisms and mechanistic knowledge in the establishment of cause-effect relationships. We differentiate causal knowledge into causes, effects, mechanisms, cause-effect relationships, and causal stories (narrative accounts of the whole process). Failing to understand the role of mechanisms in this picture, including their absence of knowledge or incorrect specification, leads to errors of wrongly excluding cause-effect relationships or wrongly inferring their existence. We highlight the primary of causality over mechanistic knowledge and causal stories in scientific reasoning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. These elements make up part of what is considered a scientific explanation. I do not address whether they are the entirety of a scientific explanation or merely a part of one. This is a large departure from some conceptions of explanations (e.g. Salmon 1994).

  2. One such definition, for example: "Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions." (Machamer et al. 2000, p. 3). It is not just the parts, but the behavior (or activities) of those parts.

  3. There is a deeper question of the possibility of instantaneity in general that is beyond the scope of this paper, though I take it as given that two events cannot occur instantaneously at a near-infinitely small scale (e.g. defined as a plank time value of 0). Some amount of time must pass between the cause and the effect. Whether pure instantaneous causation is possible is a different question, altogether.

  4. For example, the counterfactual model is logically equivalent to certain structural equation models (Galles and Pearl 1998). We could easily build a structural equation model with death as the outcome and what food eaten as exogenous variables with the errors uncorrelated (due to random assignment); this model would contain all of the relevant variables in our causal model and fulfill the requirement of causality (death responds to variations in the fish liver variable; Pearl 2009).

  5. In this manuscript I occasionally refer to the mechanism. This is not to suggest that only one mechanism may exist between a cause and effect. It is of course possible for a cause–effect relationship to follow along multiple mechanisms. Mechanisms occur in-between the cause and effect, the cause and effect are not part of the mechanism.

  6. We could add in mechanisms into our SEM (see Bollen and Pearl 2013), but they would not be necessary to establish the first step of causal inference.

  7. The fact that the mechanism of the drug was known, yet it acted contrary to its mechanism has bene referred to as Sanderson’ paradox (Sanderson 1996; Starmer 2002). This is also similar to conditional causal effects, discussed below and in Hesslow (1976).

  8. The difference comes down to what the drugs due at the singular versus the multicellular level.

  9. It has been argued that the statistical observation of the path through which one variable affects another can be used to infer cause-effect relationships (Pearl 2009). The statistical and observational requirements of this front-door approach are, however, considered to be unrealizable in real data (see Morgan and Winship 2007; Bullock et al. 2010).

  10. https://www.psychologytoday.com/blog/neuronarrative/201204/what-eating-too-much-sugar-does-your-brain. Also the book by Perlmutter (2013).

  11. This line of research is called the explanation effect; however, since the use of the term ‘explanation’ in the psychological literature is so different than the use of ‘explanation’ in philosophy, we avoid the term explanation effect for simplicity.

  12. I do not address the problem of causes without mechanisms because under this model, it does not present a problem, there is no such thing.

References

  • Anderson, C. A. (1983). Abstract and concrete data in the perseverance of social theories: When weak data lead to unshakeable beliefs. Journal of Experimental Social Psychology, 19(2), 93–108.

    Article  Google Scholar 

  • Anderson, C. A., Lepper, M. R., & Ross, L. (1980). Perseverance of social theories: The role of explanation in the persistence of discredited information. Journal of Personality and Social Psychology, 39(6), 1037–1049.

    Article  Google Scholar 

  • Anderson, C. A., & Sechler, E. S. (1986). Effects of explanation and counterexplanation on the development and use of social theories. Journal of Personality and Social Psychology, 50(1), 24–34.

    Article  Google Scholar 

  • Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441.

    Article  Google Scholar 

  • Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 301–328). New York: Springer.

    Chapter  Google Scholar 

  • Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.

    Article  Google Scholar 

  • Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558.

    Article  Google Scholar 

  • Clark, R. F., Williams, S. R., Nordt, S. P., & Manoguerra, A. S. (1999). A review of selected seafood poisonings. Undersea Hyperbolic Medicine, 26(3), 175–184.

    Google Scholar 

  • Diaconis, P., Holmes, S., & Montgomery, R. (2007). Dynamical bias in the coin toss. SIAM Review, 49(2), 211–235.

    Article  Google Scholar 

  • Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311–312.

    Article  Google Scholar 

  • Galles, D., & Pearl, J. (1998). An axiomatic characterization of causal counterfactuals. Foundations of Science, 3(1), 151–182.

    Article  Google Scholar 

  • Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(S3), S342–S353.

    Article  Google Scholar 

  • Hauzman, E. E. (2006). Semmelweis and his German contemporaries. In 40 th International congress on the history of medicine.

  • Hesslow, G. (1976). Two notes on the probabilistic approach to causality. Philosophy of Science, 43, 290–292.

    Article  Google Scholar 

  • Hesslow, G. (1981). Causality and determinism. Philosophy of Science, 48(4), 591–605.

    Article  Google Scholar 

  • Howick, J. (2011). Exposing the vanities—and a qualified defense—of mechanistic reasoning in health care decision making. Philosophy of Science, 78(5), 926–940.

    Article  Google Scholar 

  • Illari, P. M., & Williamson, J. (2012). What is a mechanism? Thinking about mechanisms across the sciences. European Journal for Philosophy of Science, 2(1), 119–135.

    Article  Google Scholar 

  • Koslowski, B. (1996). Theory and evidence: The development of scientific reasoning. Mit Press.

  • Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.

    Article  Google Scholar 

  • Mackie, J. L. (1974). The cement of the universe: A study of causation. Oxford: Clarendon.

    Google Scholar 

  • Milne, J. R., Hellestrand, K. J., Bexton, R. S., Burnett, P. J., Debbas, N. M. G., & Camm, A. J. (1984). Class 1 antiarrhythmic drugs-characteristic electrocardiographs differences when assessed by atrial and ventricular pacing. European Heart Journal, 5(2), 99–107.

    Article  Google Scholar 

  • Molteni, R., Barnard, R. J., Ying, Z., Roberts, C. K., & Gomez-Pinilla, F. (2002). A high-fat, refined sugar diet reduces hippocampal brain-derived neurotrophic factor, neuronal plasticity, and learning. Neuroscience, 112(4), 803–814.

    Article  Google Scholar 

  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Noble, D. (2008). The music of life: Biology beyond genes. Oxford: Oxford University Press.

    Google Scholar 

  • Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). Cambridge: MIT Press.

    Book  Google Scholar 

  • Pearl, J. (2014). Causes of effects, from philosophy to science. Tech. Rep.

  • Perlmutter, D. (2013). Grain brain: The surprising truth about wheat, carbs, and sugar–your brain’s silent killers. Hachette: Little, Brown and Company.

    Google Scholar 

  • Psillos, S. (2004). A glimpse of the secret connexion: Harmonizing mechanisms with counterfactuals. Perspectives on Science, 12(3), 288–319.

    Article  Google Scholar 

  • Rouder, J. N., & Morey, R. D. (2011). A Bayes factor meta-analysis of Bem’s ESP claim. Psychonomic Bulletin & Review, 18(4), 682–689.

    Article  Google Scholar 

  • Rouder, J. N., Morey, R. D., & Province, J. M. (2013). A Bayes factor meta-analysis of recent extrasensory perception experiments: Comment on Storm, Tressoldi, and Di Risio (2010). Psychological Bulletin, 139(1), 241–247.

    Article  Google Scholar 

  • Routh, C. H. F. (1849). On the causes of the endemic puerperal fever of Vienna. Medico-chirurgical Transactions, 32, 27.

    Article  Google Scholar 

  • Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, 100(469), 322–331.

    Article  Google Scholar 

  • Russo, F., & Williamson, J. (2007). Interpreting causality in the health sciences. International Studies in the Philosophy of Science, 21(2), 1157–1170.

    Article  Google Scholar 

  • Salmon, W. C. (1994). Causality without counterfactuals. Philosophy of Science, 61, 297–312.

    Article  Google Scholar 

  • Salmon, W. C. (1998). Causality and explanation. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Sanderson, J. (1996). The sword of Damocles. Lancet, 348, 2–3.

    Article  Google Scholar 

  • Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychological Bulletin, 138(4), 628–654.

    Article  Google Scholar 

  • Silverman, W. (1997). Equitable distribution of the risks and benefits associated with medical innovations. In A. Maynard & I. Chalmers (Eds.), Non-random reflections on health services research. London: BMJ.

    Google Scholar 

  • Starmer, C. F. (2002). How antiarrhythmic drugs increase the rate of sudden cardiac death. International Journal of Bifurcation and Chaos, 12(09), 1953–1968.

    Article  Google Scholar 

  • Taleb, N. N. (2010). The black swan: The impact of the highly improbable fragility. New York, NY: Random House.

    Google Scholar 

  • Trial, C. A. S. (1989). Investigators. Preliminary report: Effect of encainide and flecainide on mortality in a randomized trial of arrhythmia suppression after myocardial infarction. New England Journal of Medicine, 321(6), 406–412.

    Article  Google Scholar 

  • Trial, C. A. S., & II Investigators. (1992). Effect of the antiarrhythmic agent moricizine on survival after myocardial infarction. New England Journal of Medicine, 327(4), 227–233.

    Article  Google Scholar 

  • Williamson, J. (2013). How can causal explanations explain? Erkenntnis, 78(2), 257–275.

    Article  Google Scholar 

  • Wolraich, M. L., Wilson, D. B., & White, J. W. (1995). The effect of sugar on behavior or cognition in children. A meta-analysis. JAMA, 274(20), 1617–1621.

    Article  Google Scholar 

  • Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.

    Google Scholar 

  • Wykticky, H., & Skopec, M. (1983). Ignaz Philipp Semmelweis, the prophet ofbacteriology. Infection Control, 4(5), 367–370.

    Article  Google Scholar 

  • Xia, H., Zhao, X., Bains, J., & Wortham, D. C. (2009). Influence of channel blockers on cardiac alternans. In 2009 Annual international conference of the IEEE engineering in medicine and biology society (pp. 2823–2826). IEEE.

Download references

Acknowledgements

I would like to thank Nancy Cartwright, Mordechai Juni, Taraz Lee, Andrew Maul, Rebecca Schaefer, and Margaret Tampari for their thoughts on this argument; as well as Rogier Kievit and the two anonymous reviewers for their helpful comments on improving this paper.

Author information

Authors and Affiliations

  1. University of California, Santa Barbara, CA, USA

    John Protzko

Authors
  1. John Protzko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Protzko.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Protzko, J. Disentangling Mechanisms from Causes: And the Effects on Science. Found Sci 23, 37–50 (2018). https://doi.org/10.1007/s10699-016-9511-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10699-016-9511-x

Keywords

Search

Navigation