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Causal inference on human behaviour

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Abstract

Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

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Fig. 1: A framework for triangulating on causal effects.

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References

  1. Angrist, J. D. & Pischke, J.-S. The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. J. Econ. Perspect. 24, 3–30 (2010).

    Article  Google Scholar 

  2. Hernán, M. A. & Robins, J. M. Causal Inference: What If (Chapman & Hall/CRC, 2020).

  3. Aronow, P. M. & Miller, B. T. Foundations of Agnostic Statistics (Cambridge Univ. Press, 2019).

  4. Keele, L. The statistics of causal inference: a view from political methodology. Polit. Anal. 23, 313–335 (2015).

    Article  Google Scholar 

  5. Foster, E. M. Causal inference and developmental psychology. Dev. Psychol. 46, 1454–1480 (2010).

    Article  PubMed  Google Scholar 

  6. Marinescu, I. E., Lawlor, P. N. & Kording, K. P. Quasi-experimental causality in neuroscience and behavioural research. Nat. Hum. Behav. 2, 891–898 (2018).

    Article  PubMed  Google Scholar 

  7. Rohrer, J. M. Thinking clearly about correlations and causation: graphical causal models for observational data. Adv. Methods Pract. Psychol. Sci. 1, 27–42 (2018).

    Article  Google Scholar 

  8. Rigoux, L. & Daunizeau, J. Dynamic causal modelling of brain–behaviour relationships. NeuroImage 117, 202–221 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Gangl, M. Causal inference in sociological research. Annu. Rev. Sociol. 36, 21–47 (2010).

    Article  Google Scholar 

  10. Winship, C. & Morgan, S. L. The estimation of causal effects from observational data. Annu. Rev. Sociol. 25, 659–706 (1999).

    Article  Google Scholar 

  11. Imbens, G. W. & Rubin, D. B. Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge Univ. Press, 2015).

  12. Pearl, J. Causality: Models, Reasoning, and Inference 2nd edn (Cambridge Univ. Press, 2009).

  13. Hamaker, E. L. & Wichers, M. No time like the present. Curr. Dir. Psychol. Sci. 26, 10–15 (2017).

    Article  Google Scholar 

  14. Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, 2009).

  15. Gelman, A. & Imbens, G. Why Ask Why? Forward Causal Inference and Reverse Causal Questions Working Paper No. 19614 (NBER, 2013).

  16. Alvarez-Vargas, D. et al. Hedges, mottes, and baileys: causally ambiguous statistical language can increase perceived study quality and policy relevance. PLoS ONE 18, e0286403 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Haber, N. A. et al. Causal and associational language in observational health research: a systematic evaluation. Am. J. Epidemiol. 191, 2084–2097 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hernán, M. A. The C-word: scientific euphemisms do not improve causal inference from observational data. Am. J. Public Health 108, 616–619 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Rohrer, J. M. & Lucas, R. E. Causal effects of well-being on health: it’s complicated. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/wgbe4 (2020).

  20. Hoemann, K., Devlin, M. & Barrett, L. F. Comment: emotions are abstract, conceptual categories that are learned by a predicting brain. Emot. Rev. 12, 253–255 (2020).

    Article  Google Scholar 

  21. Young, C. & Holsteen, K. Model uncertainty and robustness: a computational framework for multimodel analysis. Sociol. Methods Res. 46, 3–40 (2017).

    Article  Google Scholar 

  22. Cinelli, C. & Hazlett, C. Making sense of sensitivity: extending omitted variable bias. J. R. Stat. Soc. B 82, 39–67 (2020).

    Article  Google Scholar 

  23. Branwen, G. How often does correlation = causality? Gwern.net https://www.gwern.net/Correlation (2022).

  24. Runge, J. Causal network reconstruction from time series: from theoretical assumptions to practical estimation. Chaos 28, 075310 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Oster, E. Health recommendations and selection in health behaviors. Am. Econ. Rev. Insights 2, 143–160 (2020).

    Article  Google Scholar 

  26. VanderWeele, T. J. Constructed measures and causal inference: towards a new model of measurement for psychosocial constructs. Epidemiology 33, 141–151 (2022).

    Article  PubMed  Google Scholar 

  27. Greenland, S., Judea, P. & Robins, J. M. Causal diagrams for epidemiologic research. Epidemiology 10, 37–48 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Rosenbaum, P. R. From association to causation in observational studies: the role of tests of strongly ignorable treatment assignment. J. Am. Stat. Assoc. 79, 41–48 (1984).

    Article  Google Scholar 

  29. Hoyle, R. H., Lynam, D. R., Miller, J. D. & Pek, J. The questionable practice of partialing to refine scores on and inferences about measures of psychological constructs. Annu. Rev. Clin. Psychol. 19, 155–176 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cinelli, C., Forney, A. & Pearl, J. A crash course in good and bad controls. Sociol. Methods Res. https://doi.org/10.1177/00491241221099552 (2022).

  31. Wysocki, A. C., Lawson, K. M. & Rhemtulla, M. Statistical control requires causal justification. Adv. Methods Pract. Psychol. Sci. 5, 251524592210958 (2022).

    Article  Google Scholar 

  32. Elwert, F. & Winship, C. Endogenous selection bias: the problem of conditioning on a collider variable. Annu. Rev. Sociol. 40, 31–53 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Knox, D., Lowe, W. & Mummolo, J. Administrative records mask racially biased policing. Am. Polit. Sci. Rev. 114, 619–637 (2020).

    Article  Google Scholar 

  34. Bryan, C. J., Tipton, E. & Yeager, D. S. Behavioural science is unlikely to change the world without a heterogeneity revolution. Nat. Hum. Behav. 5, 980–989 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Haslbeck, J. M. B. & Ryan, O. Recovering within-person dynamics from psychological time series. Multivar. Behav. Res. 57, 735–766 (2022).

    Article  Google Scholar 

  36. Goldsmith-Pinkham, P., Hull, P. & Kolesár, M. Contamination Bias in Linear Regressions Working Paper No. 30108 (NBER, 2022).

  37. Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econ. 225, 254–277 (2021).

    Article  Google Scholar 

  38. Wu, W., Carroll, I. A. & Chen, P.-Y. A single-level random-effects cross-lagged panel model for longitudinal mediation analysis. Behav. Res Methods 50, 2111–2124 (2018).

    Article  PubMed  Google Scholar 

  39. Rubin, D. B. Causal inference using potential outcomes. J. Am. Stat. Assoc. 100, 322–331 (2005).

    Article  CAS  Google Scholar 

  40. Altmejd, A. et al. O brother, where start thou? Sibling spillovers on college and major choice in four countries. Q. J. Econ. 136, 1831–1886 (2021).

    Article  Google Scholar 

  41. Heckman, J. & Karapakula, G. Intergenerational and Intragenerational Externalities of the Perry Preschool Project Working Paper No. 25889 (NBER, 2019).

  42. Karbownik, K. & Özek, U. Setting a Good Example? Examining Sibling Spillovers in Educational Achievement Using a Regression Discontinuity Design Working Paper No. 26411 (NBER, 2019).

  43. Bringmann, L. F. et al. Psychopathological networks: theory, methods and practice. Behav. Res Ther. 149, 104011 (2022).

    Article  PubMed  Google Scholar 

  44. Dietrich, J., Schmiedek, F. & Moeller, J. Academic motivation and emotions are experienced in learning situations, so let’s study them: introduction to the special issue. Learn. Instr. 81, 101623 (2022).

    Article  Google Scholar 

  45. Robins, J. M., Scheines, R., Spirtes, P. & Wasserman, L. Uniform consistency in causal inference. Biometrika 90, 491–515 (2003).

    Article  Google Scholar 

  46. VanderWeele, T. J. & Hernán, M. A. Causal inference under multiple versions of treatment. J. Causal Inference 1, 1–20 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Pearl, J. Does obesity shorten life? Or is it the soda? On non-manipulable causes. J. Causal Inference 6, 20182001 (2018).

    Article  Google Scholar 

  48. Angrist, J. D. & Pischke, J.-S. Mastering ’Metrics: The Path from Cause to Effect (Princeton Univ. Press, 2014).

  49. Eronen, M. I. Causal discovery and the problem of psychological interventions. N. Ideas Psychol. 59, 100785 (2020).

    Article  Google Scholar 

  50. Scheines, R. The similarity of causal inference in experimental and non-experimental studies. Phil. Sci. 72, 927–940 (2005).

    Article  Google Scholar 

  51. Bringmann, L. F., Elmer, T. & Eronen, M. I. Back to basics: the importance of conceptual clarification in psychological science. Curr. Dir. Psychol. Sci. 31, 340–346 (2022).

    Article  Google Scholar 

  52. Spirtes, P. & Scheines, R. Causal inference of ambiguous manipulations. Phil. Sci. 71, 833–845 (2004).

    Article  Google Scholar 

  53. Bollen, K. A. & Brand, J. E. A general panel model with random and fixed effects: a structural equations approach. Soc. Forces 89, 1–34 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hamaker, E. L., Kuiper, R. M. & Grasman, R. P. P. P. A critique of the cross-lagged panel model. Psychol. Methods 20, 102–116 (2015).

    Article  PubMed  Google Scholar 

  55. Zyphur, M. J. et al. From data to causes I: building a general cross-lagged panel model (GCLM). Organ. Res. Methods 23, 651–687 (2020).

    Article  Google Scholar 

  56. Voelkle, M. C., Oud, J. H. L., Davidov, E. & Schmidt, P. An SEM approach to continuous time modeling of panel data: relating authoritarianism and anomia. Psychol. Methods 17, 176–192 (2012).

    Article  PubMed  Google Scholar 

  57. Frangakis, C. E. & Rubin, D. B. Principal stratification in causal inference. Biometrics 58, 21–29 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Beltz, A. M. & Gates, K. M. Network mapping with GIMME. Multivar. Behav. Res. 52, 789–804 (2017).

    Article  Google Scholar 

  59. Montoya, L. M. et al. The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions. International J. Biostat. 19, 217–238 (2023).

    Article  Google Scholar 

  60. Gische, C. & Voelkle, M. C. Beyond the mean: a flexible framework for studying causal effects using linear models. Psychometrika 87, 868–901 (2022).

    Article  PubMed  Google Scholar 

  61. Imai, K. & Kim, I. S. When should we use unit fixed effects regression models for causal inference with longitudinal data? Am. J. Polit. Sci. 63, 467–490 (2019).

    Article  Google Scholar 

  62. Sobel, M. E. & Lindquist, M. A. Causal inference for fMRI time series data with systematic errors of measurement in a balanced on/off study of social evaluative threat. J. Am. Stat. Assoc. 109, 967–976 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Usami, S. Within-person variability score-based causal inference: a two-step estimation for joint effects of time-varying treatments. Psychometrika 88, 1466–1494 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Hamaker, E. L., Mulder, J. D. & van IJzendoorn, M. H. Description, prediction and causation: methodological challenges of studying child and adolescent development. Dev. Cogn. Neurosci. 46, 100867 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Lundberg, I., Johnson, R. & Stewart, B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am. Sociol. Rev. 86, 532–565 (2021).

    Article  Google Scholar 

  66. Rohrer, J. M. & Murayama, K. These are not the effects you are looking for: causality and the within-/between-persons distinction in longitudinal data analysis. Adv. Methods Pract. Psychol. Sci. 6, 251524592211408 (2023).

    Article  Google Scholar 

  67. Silberzahn, R. et al. Many analysts, one data set: making transparent how variations in analytic choices affect results. Adv. Methods Pract. Psychol. Sci. 1, 337–356 (2018).

    Article  Google Scholar 

  68. Auspurg, K. & Brüderl, J. Has the credibility of the social sciences been credibly destroyed? Reanalyzing the ‘many analysts, one data set’ project. Socius 7, 237802312110244 (2021).

    Article  Google Scholar 

  69. Shadish, W. R, Cook, T. D & Campbell, D. T. Experimental and Quasi-Experimental Designs for Generalized Causal Inference (Houghton, Mifflin, 2002).

  70. Rhemtulla, M., van Bork, R. & Borsboom, D. Worse than measurement error: consequences of inappropriate latent variable measurement models. Psychol. Methods 25, 30–45 (2020).

    Article  PubMed  Google Scholar 

  71. Westfall, J. & Yarkoni, T. Statistically controlling for confounding constructs is harder than you think. PLoS ONE 11, e0152719 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Grosz, M. P., Rohrer, J. M. & Thoemmes, F. The taboo against explicit causal inference in nonexperimental psychology. Perspect. Psychol. Sci. 15, 1243–1255 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Deming, D. Early childhood intervention and life-cycle skill development: evidence from Head Start. Am. Econ. J. Appl. Econ. 1, 111–134 (2009).

    Article  Google Scholar 

  74. Pion, G. M. & Lipsey, M. W. Impact of the Tennessee Voluntary Prekindergarten Program on children’s literacy, language, and mathematics skills: results from a regression-discontinuity design. AERA Open 7, 233285842110413 (2021).

    Article  Google Scholar 

  75. Ritchie, S. J. & Tucker-Drob, E. M. How much does education improve intelligence? A meta-analysis. Psychol. Sci. 29, 1358–1369 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Steiner, P. M., Wong, V. C. & Anglin, K. A causal replication framework for designing and assessing replication efforts. Z. Psychol. 227, 280–292 (2019).

    Google Scholar 

  77. Munafò, M. R. & Davey Smith, G. Robust research needs many lines of evidence. Nature 553, 399–401 (2018).

    Article  PubMed  Google Scholar 

  78. Colnet, B. et al. Causal inference methods for combining randomized trials and observational studies: a review. Stat. Sci. 39, 165–191 (2024).

    Article  Google Scholar 

  79. Wan, S., Brick, T. R., Alvarez-Vargas, D. & Bailey, D. H. Triangulating on developmental models with a combination of experimental and nonexperimental estimates. Dev. Psychol. 59, 216–228 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Gische, C., West, S. G. & Voelkle, M. C. Forecasting causal effects of interventions versus predicting future outcomes. Struct. Equ. Modeling 28, 475–492 (2021).

    Article  PubMed  Google Scholar 

  81. Imai, K., Kim, I. S. & Wang, E. H. Matching methods for causal inference with time‐series cross‐sectional data. Am. J. Polit. Sci. 67, 587–605 (2021).

    Article  Google Scholar 

  82. Zyphur, M. J. et al. From data to causes II: comparing approaches to panel data analysis. Organ. Res. Methods 23, 688–716 (2020).

    Article  Google Scholar 

  83. Lüdtke, O. & Robitzsch, A. A comparison of different approaches for estimating cross-lagged effects from a causal inference perspective. Struct. Equ. Modeling 29, 888–907 (2022).

    Article  Google Scholar 

  84. Usami, S., Murayama, K. & Hamaker, E. L. A unified framework of longitudinal models to examine reciprocal relations. Psychol. Methods 24, 637–657 (2019).

    Article  PubMed  Google Scholar 

  85. Bond, T. N. & Lang, K. The evolution of the black–white test score gap in grades K–3: the fragility of results. Rev. Econ. Stat. 95, 1468–1479 (2013).

    Article  Google Scholar 

  86. Larzelere, R. E., Cox, R. B. & Smith, G. L. Do nonphysical punishments reduce antisocial behavior more than spanking? A comparison using the strongest previous causal evidence against spanking. BMC Pediatr. 10, 10 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Oster, E. Unobservable selection and coefficient stability: theory and evidence. J. Bus. Econ. Stat. 37, 187–204 (2019).

    Article  Google Scholar 

  88. Athey, S., Chetty, R., Imbens, G. W. & Kang, H. The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely Working Paper No. 26463 (NBER, 2019).

  89. Weidmann, B. & Miratrix, L. Lurking inferential monsters? Quantifying selection bias in evaluations of school programs. J. Policy Anal. Manage. 40, 964–986 (2021).

    Article  Google Scholar 

  90. Dehejia, R. H. & Wahba, S. Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. J. Am. Stat. Assoc. 94, 1053–1062 (1999).

    Article  Google Scholar 

  91. LaLonde, R. J. Evaluating the econometric evaluations of training programs with experimental data. Am. Econ. Rev. 76, 604–620 (1986).

    Google Scholar 

  92. Protzko, J. Effects of cognitive training on the structure of intelligence. Psychon. Bull. Rev. 24, 1022–1031 (2017).

    Article  PubMed  Google Scholar 

  93. Schmidt, F. L. Beyond questionable research methods: the role of omitted relevant research in the credibility of research. Arch. Sci. Psychol. 5, 32–41 (2017).

    Google Scholar 

  94. Meehl, P. E. Why summaries of research on psychological theories are often uninterpretable. Psychol. Rep. 66, 195–244 (1990).

    Article  Google Scholar 

  95. Chaku, N., Kelly, D. P. & Beltz, A. M. Individualized learning potential in stressful times: how to leverage intensive longitudinal data to inform online learning. Comput. Hum. Behav. 121, 106772 (2021).

    Article  Google Scholar 

  96. Moeller, J. et al. Generalizability crisis meets heterogeneity revolution: determining under which boundary conditions findings replicate and generalize. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/5wsna (2022).

  97. Dunning, T. et al. (eds). Information, Accountability, And Cumulative Learning: Lessons From Metaketa I (Cambridge Univ. Press, 2019).

  98. Low, H. & Meghir, C. The use of structural models in econometrics. J. Econ. Perspect. 31, 33–58 (2017).

    Article  Google Scholar 

  99. Todd, P. E. & Wolpin, K. I. Assessing the impact of a school subsidy program in Mexico: using a social experiment to validate a dynamic behavioral model of child schooling and fertility. Am. Econ. Rev. 96, 1384–1417 (2006).

    Article  PubMed  Google Scholar 

  100. Pearl, J., Glymour, M. & Jewell, N. P. Causal Inference in Statistics: A Primer (John Wiley & Sons, 2016).

  101. Achen, C. H. Let’s put garbage-can regressions and garbage-can probits where they belong. Confl. Manage. Peace Sci. 22, 327–339 (2005).

    Article  Google Scholar 

  102. Athey, S. & Imbens, G. Recursive partitioning for heterogeneous causal effects. Proc. Natl Acad. Sci. USA 113, 7353–7360 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Geng, E. H., Holmes, C. B., Moshabela, M., Sikazwe, I. & Petersen, M. L. Personalized public health: an implementation research agenda for the HIV response and beyond. PLoS Med. 16, e1003020 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Moeller, J. Averting the next credibility crisis in psychological science: within-person methods for personalized diagnostics and intervention. J. Pers. Oriented Res. 7, 53–77 (2021).

    Article  PubMed  Google Scholar 

  105. Pearl, J. & Bareinboim, E. Transportability of causal and statistical relations: a formal approach. Proc. AAAI Conf. Artif. Intell. 25, 247–254 (2011).

    Google Scholar 

  106. Wager, S. & Athey, S. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113, 1228–1242 (2018).

    Article  CAS  Google Scholar 

  107. Benjamin-Chung, J. et al. Spillover effects in epidemiology: parameters, study designs and methodological considerations. Int. J. Epidemiol. 47, 332–347 (2018).

    Article  PubMed  Google Scholar 

  108. Hudgens, M. G. & Halloran, M. E. Toward causal inference with interference. J. Am. Stat. Assoc. 103, 832–842 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Imai, K., Jiang, Z. & Malani, A. Causal inference with interference and noncompliance in two-stage randomized experiments. J. Am. Stat. Assoc. 116, 632–644 (2021).

    Article  CAS  Google Scholar 

  110. Tchetgen, E. J. T. & VanderWeele, T. J. On causal inference in the presence of interference. Stat. Methods Med. Res. 21, 55–75 (2012).

    Article  Google Scholar 

  111. Zhang, C., Mohan, K. & Pearl, J. Causal inference with non-IID data using linear graphical models. Adv. Neural Inf. Process. Syst. 35, 13214–13225 (2022).

    Google Scholar 

  112. Eberhardt, F. & Scheines, R. Interventions and causal inference. Phil. Sci. 74, 981–995 (2007).

    Article  Google Scholar 

  113. Mooij, J. M., Magliacane, S. & Claassen, T. Joint causal inference from multiple contexts. J. Mach. Learn. Res. 21, 3919–4026 (2020).

    Google Scholar 

  114. Peters, J., Bühlmann, P. & Meinshausen, N. Causal inference by using invariant prediction: identification and confidence intervals. J. R. Stat. Soc. B 78, 947–1012 (2016).

    Article  Google Scholar 

  115. Aalen, O., Røysland, K., Gran, J., Kouyos, R. & Lange, T. Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms. Stat. Methods Med. Res. 25, 2294–2314 (2016).

    Article  CAS  PubMed  Google Scholar 

  116. Driver, C. C. & Voelkle, M. C. in Continuous Time Modeling in the Behavioral and Related Sciences (eds Van Montfort, K. et al.) 79–109 (Springer International, 2018).

  117. Røysland, K. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915 (2011).

    Article  Google Scholar 

  118. Ryan, O. & Hamaker, E. L. Time to intervene: a continuous-time approach to network analysis and centrality. Psychometrika 87, 214–252 (2022).

    Article  PubMed  Google Scholar 

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Acknowledgements

This Review resulted from a cross-disciplinary workshop discussing such approaches (https://www.longitudinaldataanalysis.com/). The workshop and collaboration were funded by the Jacobs Foundation and CIFAR. The funders had no role in the decision to publish or in the preparation of this manuscript.

Author information

Authors and Affiliations

  1. School of Education, University of California, Irvine, Irvine, CA, USA

    Drew H. Bailey

  2. Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany

    Alexander J. Jung & Kou Murayama

  3. Department of Psychology, University of Michigan, Ann Arbor, MI, USA

    Adriene M. Beltz

  4. Department of Theoretical Philosophy, University of Groningen, Groningen, the Netherlands

    Markus I. Eronen

  5. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany

    Christian Gische

  6. Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, the Netherlands

    Ellen L. Hamaker

  7. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Konrad P. Kording

  8. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA

    Konrad P. Kording

  9. Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada

    Catherine Lebel

  10. Department of Radiology, University of Calgary, Calgary, Alberta, Canada

    Catherine Lebel

  11. Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA

    Martin A. Lindquist

  12. Leipzig University, Leipzig, Germany

    Julia Moeller

  13. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia

    Adeel Razi

  14. Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia

    Adeel Razi

  15. Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK

    Adeel Razi

  16. CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario, Canada

    Adeel Razi

  17. Wilhelm Wundt Institute for Psychology, Faculty of Life Sciences, Leipzig University, Leipzig, Germany

    Julia M. Rohrer

  18. Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA

    Baobao Zhang

  19. Research Institute, Kochi University of Technology, Kochi, Japan

    Kou Murayama

Authors
  1. Drew H. Bailey
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  2. Alexander J. Jung
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  5. Christian Gische
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  8. Catherine Lebel
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  9. Martin A. Lindquist
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  10. Julia Moeller
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  12. Julia M. Rohrer
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  13. Baobao Zhang
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  14. Kou Murayama
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Corresponding author

Correspondence to Drew H. Bailey.

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The authors declare no competing interests.

Peer review

Peer review information

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Bailey, D.H., Jung, A.J., Beltz, A.M. et al. Causal inference on human behaviour. Nat Hum Behav 8, 1448–1459 (2024). https://doi.org/10.1038/s41562-024-01939-z

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