Causal discovery and the problem of psychological interventions
Open accessAbstract
Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the intervention in fact did, is difficult to reliably estimate. A further complication is that the causal findings in psychology are typically made at the population level, and such findings do not allow inferences to individual-level causal relationships. I also discuss the implications of these problems for research, as well as various ways of addressing them, such as focusing more on the discovery of robust but non-causal patterns.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Media comparison studies that introduce new media alongside new instructional methods, interface designs, or activity structures fail to surgically remove or add the variable of interest without altering the broader instructional mix, rendering their results unfit for interpretation as intended.
“Such studies fail to surgically remove or add the variable of interest without altering the broader instructional mix, rendering their results unfit for interpretation, at least in the way intended by their authors.”
From <scp>ChatGPT</scp> in Education: An Effect in Search of a Cause (2025) · cites this paper as Eronen (2020)
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436 · see citing works
On the psychology of prediction.
Kahneman, D., & Tversky, A. (1973) · Psychological Review · doi:10.1037/h0034747 · Cited by 6,202 · see citing works
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293 · see citing works
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359 · see citing works
Causal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336 · cited 3× · see citing works
“…form 11 February 2020; Accepted 13 February 2020 New Ideas in Psychology 59 (2020) 100785 the approach developed by Spirtes et al. (2000) and Pearl (2000, 2009), which provides a theory of representing causal relationships as directed acyclic graphs (DAGs) and modeling them as causal Bayes…”
BackgroundCausal Inference Using Potential Outcomes: Design, Modeling, Decisions
Rubin, D. B. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001880 · Cited by 2,173 · see citing works
Lacking control increases illusory pattern perception
Whitson, J. A., & Galinsky, A. D. (2008) · Science · doi:10.1126/science.1159845 · Cited by 1,119 · see citing works
Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002 · cited 2× · see citing works
“…the use of propensity scores (i.e., propensity scores are calculated and individuals with similar scores are matched and compared; Rubin, 2005; Rohrer, 2018), instrumental variables (e.g., Hogan & Lancaster, 2004) or causal discovery algorithms (e.g., Malinsky & Danks, 2018; Spirtes et al.,…”
BackgroundMethodA Theory of Causation in the Social and Biological Sciences
Reutlinger, A. (2013) · doi:10.1057/9781137281043 · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 48
- Baumgartner, M. (2018). The inherent empirical underdetermination of mental causation. Australasian Journal of Philosophy.
- Baumgartner, M., & Gebharter, A. (2016). Constitutive relevance, mutual manipulability, and fat-handedness. The British Journal for the Philosophy of Science, 67, 731-756.
- Bechtel, W. (2008). Mental Mechanisms. Philosophical Perspectives on Cognitive Neuroscience. London: Routledge.
- Beebee, H., Hitchcock, C., & Menzies, P. (2009). The Oxford handbook of causation. Oxford: Oxford University Press.
- Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge: Cambridge University Press.
- Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203.
- Bringmann, L. F., & Eronen, M. I. (2016). Heating up the measurement debate: What psychologists can learn from the history of physics. Theory & Psychology, 26(1), 27-43.
- Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54(4), 297.
- Campbell, J. (2007). An interventionist approach to causation in psychology. In A. Gopnik, & L. Schulz (Eds.), Causal learning. Psychology, philosophy, and computation (pp. 58-66). Oxford: Oxford University Press.
- Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
- Cartwright, N. (1989). Nature's capacities and their measurement. Oxford: Clarendon Press.
- Colombo, D., Maathuis, M. H., Kalisch, M., & Richardson, T. S. (2012). Learning high- dimensional directed acyclic graphs with latent and selection variables. Annals of Statistics, 294-321.
- Cook, T. D., Scriven, M., Coryn, C. L., & Evergreen, S. D. (2010). Contemporary thinking about causation in evaluation: A dialogue with. American Journal of Evaluation, 31 (1), 105-117.
- Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281.
- Dowe, P. (2000). Physical causation. New York: Cambridge University Press.
- Eberhardt, F. (2014). Direct causes and the trouble with soft interventions. Erkenntnis, 79 (4), 755-777.
- Eronen, M. I. (2015). Robustness and reality. Synthese, 192(12), 3961-3977.
- Eronen, M. I. (2020). Interventionism for the intentional stance: True believers and their brains. Topoi, 39(1), 45-55.
- Haig, B. D. (2013). Detecting psychological phenomena: Taking bottom-up research seriously. American Journal of Psychology, 126(2), 135-153.
- Hamaker, E. L. (2011). Why researchers should think "within-person". In M. R. Mehl, & T. A. Conner (Eds.), Handbook of research methods for studying daily life (pp. 43-61). New York, NY: Guilford Press.
- Hogan, J. W., & Lancaster, T. (2004). Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies. Statistical Methods in Medical Research, 13(1), 17-48.
- Illari, P., & Russo, F. (2014). Causality. Philosophical theory meets scientific practice. Oxford: Oxford University Press.
- de Leon, J. (2012). Evidence-based medicine versus personalized medicine: Are they enemies? Journal of Clinical Psychopharmacology, 32(2), 153-164.
- Lewis, D. (1973). Causation. The Journal of Philosophy, 70, 556-567.
- Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011). The n-of- 1 clinical trial: The ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173.
- Malinsky, D., & Danks, D. (2018). Causal discovery algorithms: A practical guide. Philosophy Compass, 13(1), e12470.
- Mumford, S., & Anjum, R. L. (2011). Getting causes from powers. Oxford: Oxford University Press.
- Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.
- Northcott, R. (2019). Free will is not a testable hypothesis. Erkenntnis, 84(3), 617-631.
- Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., … Kuppens, P. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3(2), 292-300.
- Peters, J., Bühlmann, P., & Meinshausen, N. (2016). Causal inference by using invariant prediction: Identification and confidence intervals. Journal of the Royal Statistical Society B, 78, 947-1012. https://doi.org/10.1111/rssb.12167. · DOI
- Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3), 283-311.
- Romero, F. (2015). Why there isn't inter-level causation in mechanisms. Synthese, 192 (11), 3731-3755.
- Rozin, P. (2001). Social psychology and science: Some lessons from Solomon Asch. Personality and Social Psychology Review, 5(1), 2-14.
- Scheines, R. (2005). The similarity of causal inference in experimental and non- experimental studies. Philosophy of Science, 72(5), 927-940.
- Shadish, W. R., & Rindskopf, D. M. (2007). Methods for evidence-based practice: Quantitative synthesis of single-subject designs. New Directions for Evaluation, 2007 (113), 95-109.
- Shadish, W. R., & Sullivan, K. J. (2012). Theories of causation in psychological science. In H. Cooper, et al. (Eds.), APA handbook of research methods in psychology, Vol 1: Foundations, planning, measures, and psychometrics (pp. 23-52). Washington, DC: American Psychological Association.
- Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28(9), 1059-1074.
- Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. New York: Springer.
- Tabb, K., & Schaffner, K. F. (2017). Causal pathways, random walks and tortuous paths: Moving from the descriptive to the etiological in psychiatry. In K. S. Kendler, & J. Parnas (Eds.), Philosophical issues in psychiatry IV: Nosology (pp. 342-360). Oxford: Oxford University Press.
- Weinberger, N. (2015). If intelligence is a cause, it is a within-subjects cause. Theory & Psychology, 25(3), 346-361.
- West, S. G., & Thoemmes, F. (2010). Campbell's and Rubin's perspectives on causal inference. Psychological Methods, 15(1), 18.
- Wimsatt, W. C. (1981). Robustness, reliability, and overdetermination. In M. Brewer, & B. Collins (Eds.), Scientific inquiry and the social sciences (pp. 124-163). San Francisco, CA: Jossey-Bass.
- Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA: Harvard University.
- Woodward, J. (2003). Making things happen. A theory of causal explanation. Oxford: Oxford University Press.
- Woodward, J. (2008a). Mental causation and neural mechanisms. In J. Hohwy, & J. Kallestrup (Eds.), Being reduced: New essays on reduction, explanation, and causation (pp. 218-262). Oxford: Oxford University Press.
- Woodward, J., & Hitchcock, C. (2003). Explanatory generalizations, Part I: A counterfactual account. Noûs, 37(1), 1-24.
- Zhang, J. (2006). Causal inference and reasoning in causally insufficient systems.. Doctoral dissertation. Carnegie Mellon University.
Cited by
Sorted by citations
That’s a Lot to Process! Pitfalls of Popular Path Models
Rohrer, J. M., Hünermund, P., Arslan, R. C., & Elson, M. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221095827 · Cited by 267 · see citing works
Roefs, A., Fried, E. I., Kindt, M., Martijn, C., Elzinga, B., Evers, A. W. M., Wiers, R. W., Borsboom, D., & Jansen, A. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104096 · Cited by 185 · see citing works
<scp>ChatGPT</scp> in Education: An Effect in Search of a Cause
Weidlich, J., Gašević, D., Drachsler, H., & Kirschner, P. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70105 · Cited by 52 · see citing works
A network approach to the five-facet model of mindfulness
Heeren, A., Lannoy, S., Coussement, C., Hoebeke, Y., Verschuren, A., Blanchard, M. A., Chakroun-Baggioni, N., Philippot, P., & Gierski, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-94151-2 · Cited by 39 · see citing works
- Authors
- Eronen, M. I.
- Affiliations
- University of Groningen (NL)
- Corresponding author
- Eronen, Markus I.
- ORCID
- Eronen, Markus I.
- Venue
- New Ideas in Psychology
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 59
- Pages
- 100785
- ISSN
- 0732-118X (ISSN-L)
- OpenAlex
- W2899501248
- Cited by
- 102
- References
- 9
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 22 · 2023: 10 · 2022: 12 · 2021: 12 · 2020: 3 · 2019: 1
- FWCI
- 34.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- New Ideas in Psychology (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- PhilSci-Archive (University of Pittsburgh) (acceptedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- causal discovery, psychological intervention, interventionist approach, causal inference, measurement error
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review