Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Open accessAbstract
Correlation does not imply causation; but often, observational data are the only option, even though the research question at hand involves causality. This article discusses causal inference based on observational data, introducing readers to graphical causal models that can provide a powerful tool for thinking more clearly about the interrelations between variables. Topics covered include the rationale behind the statistical control of third variables, common procedures for statistical control, and what can go wrong during their implementation. Certain types of third variables—colliders and mediators—should not be controlled for because that can actually move the estimate of an association away from the value of the causal effect of interest. More subtle variations of such harmful control include using unrepresentative samples, which can undermine the validity of causal conclusions, and statistically controlling for mediators. Drawing valid causal inferences on the basis of observational data is not a mechanistic procedure but rather always depends on assumptions that require domain knowledge and that can be more or less plausible. However, this caveat holds not only for research based on observational data, but for all empirical research endeavors.
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“We recommend using approaches such as directed acyclic graphs [125,126] to help to identify when controlling for variables in the statistical analysis is appropriate, and what variables to control for (see [127] for primer on creating acyclic graphs, dealing with measurement error, and statistically controlling for va…”
MethodStudying Behaviour Change Mechanisms under Complexity
Heino, M. T. J., Knittle, K. P., Noone, C., Hasselman, F., & Hankonen, N. E. (2021) · Behavioral Sciences · doi:10.3390/bs11050077 · Cited by 152 · see citing works
“, mixing up mediators, confounders and colliders [14,15] or lowering the already worrisome statistical power [16]), try to incorporate non-linear effects [17], and add more time points (for caveats regarding latent variable modelling, see, e.”
BackgroundNetwork analysis: An overview for mental health research
Briganti, G., Scutari, M., Epskamp, S., Borsboom, D., Hoekstra, R. H. A., Golino, H. F., Christensen, A. P., Morvan, Y., Ebrahimi, O. V., Costantini, G., et al. (2024) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.2034 · Cited by 161 · see citing works
“Specifically, conditioning on a collider can open a previously closed path in the network, potentially leading to biased estimates and false causal interpretations (Rohrer, 2018).”
Giudice, M. D., & Gangestad, S. W. (2021) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245920954925 · Cited by 140 · see citing works
“The figure depicts the hypothesized causal relations among the variables, in the form of a directed acyclic graph (DAG; see Elwert, 2013; Pearl et al., 2016; Rohrer, 2018).”
BackgroundMethodInfluentialObtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts
Wiernik, B. M., & Dahlke, J. A. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245919885611 · Cited by 135 · see citing works
“If a sample of highly successful students is selected, ability and effort will become artifactually strongly negatively correlated because of indirect range restriction, or what is sometimes referred to as conditioning on a collider (Rohrer, 2018).”
BackgroundMethodInfluentialThe Cambridge Handbook of Personality Psychology
(2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822 · Cited by 140 · see citing works
A Causal Framework for Cross-Cultural Generalizability
Deffner, D., Rohrer, J. M., & McElreath, R. L. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221106366 · Cited by 152 · see citing works
“Multiple comprehensive yet accessible introductions to DAGs are available (Elwert, 2013; Pearl et al., 2016; Pearl & Mackenzie, 2018; Rohrer, 2018); thus, we focus only on the essentials.”
BackgroundInfluentialSelf-citationPreregistration of secondary data analysis: A template and tutorial
van den Akker, O. R., Weston, S. J., Campbell, L., Chopik, B., Damian, R. I., Davis-Kean, P. E., Hall, A., Kosie, J. E., Kruse, E. T., Olsen, J., et al. (2021) · Meta-Psychology · doi:10.15626/mp.2020.2625 · Cited by 131 · see citing works
Anetor, F. O., Aziegbe-Esho, E., & Verhoef, G. (2020) · Cogent Economics & Finance · doi:10.1080/23322039.2020.1737347 · Cited by 126 · see citing works
“To infer causation, statistical models have to be causal models rather than just correlation effect models (Rohrer, 2018).”
MethodSibling Constructs: What Are They, Why Do They Matter, and How Should You Handle Them?
Lawson, K. M., & Robins, R. W. (2021) · Personality and Social Psychology Review · doi:10.1177/10888683211047101 · Cited by 144 · see citing works
“A DAG is a figure—similar to a path diagram—that depicts a hypothesized causal model (see Figure 5 for examples and Rohrer, 2018 for details on DAGs in psychology).”
MethodPolygenic scores: prediction versus explanation
Plomin, R. J., & von Stumm, S. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01348-y · Cited by 121 · see citing works
Time spent playing video games is unlikely to impact well-being
Vuorre, M., Johannes, N., Magnusson, K., & Przybylski, A. K. (2022) · Royal Society Open Science · doi:10.1098/rsos.220411 · Cited by 121 · see citing works
Big little lies: a compendium and simulation of p -hacking strategies
Stefan, A. M., & Schönbrodt, F. D. (2023) · Royal Society Open Science · doi:10.1098/rsos.220346 · Cited by 149 · see citing works
Automating the design-build-test-learn cycle towards next-generation bacterial cell factories
Gurdo, N., Volke, D. C., McCloskey, D., & Nikel, P. I. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.01.002 · Cited by 117 · see citing works
Best Practices in Supervised Machine Learning: A Tutorial for Psychologists
Pargent, F., Schoedel, R., & Stachl, C. (2023) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231162559 · Cited by 124 · see citing works
“Recent calls in psychology urge to take questions of causal inference seriously, and intuitive tutorials on choosing the right control variables in statistical modeling are now available (Deffner et al., 2022; Rohrer, 2018; Wysocki et al., 2022).”
Accounting for Confounding in Observational Studies
D'Onofrio, B. M., Sjölander, A., Lahey, B. B., Lichtenstein, P., & Öberg, A. S. (2020) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045030 · Cited by 108 · see citing works
Turchin, P., Whitehouse, H., Larson, J., Cioni, E., Reddish, J., Hoyer, D., Savage, P. E., Covey, R. A., Baines, J., Altaweel, M. R., et al. (2022) · Religion Brain & Behavior · doi:10.1080/2153599x.2022.2065345 · Cited by 104 · see citing works
“SPC1 is not causal and, thus, in a sense, spurious (Rohrer, 2018), arising because both processes are driven by a similar set of causal factors.”
BackgroundA many-analysts approach to the relation between religiosity and well-being
Hoogeveen, S., Sarafoglou, A., Aczél, B., Aditya, Y., Alayan, A. J., Allen, P., Altay, S., Alzahawi, S., Amir, Y., Anthony, F.-V., et al. (2022) · Religion Brain & Behavior · doi:10.1080/2153599x.2022.2070255 · Cited by 129 · see citing works
Hamaker, E. L., Mulder, J. D., & van IJzendoorn, M. H. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100867 · Cited by 117 · see citing works
“…DAGs to decide whether one should use baseline adjustment in change analysis (see also: Pearl, 2016; Kim and Steiner, 2019; Tennant et al., 2019); Rohrer (2018) discusses DAG methodology in psychology; Lee (2012) presents an extensive account of DAG methodology in the context of personality…”
InfluentialCredibility Beyond Replicability: Improving the Four Validities in Psychological Science
Vazire, S., Schiavone, S. R., & Bottesini, J. G. (2022) · Current Directions in Psychological Science · doi:10.1177/09637214211067779 · Cited by 102 · see citing works
“…to this issue, too, seems to be growing: A recent article on “thinking clearly about correlations and causation,” which is already widely cited and discussed, has provided an overview of the challenges of causal inference in observational studies and possible solutions to them (Rohrer, 2018).”
BackgroundBottom Up Construction of a Personality Taxonomy
Condon, D. M., Wood, D., Mõttus, R., Booth, T., Costantini, G., Greiff, S., Johnson, W., Lukaszewski, A. W., Murray, A. L., Revelle, W., et al. (2020) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000626 · Cited by 94 · see citing works
Pollet, T. V., & Saxton, T. K. (2019) · Evolutionary Psychological Science · doi:10.1007/s40806-019-00192-2 · Cited by 89 · see citing works
“From a statistical point of view, restricted sampling can lead to selection bias which in turn can lead to confounding (Bareinboim and Pearl 2012; Elwert and Winship 2014; Fiedler 2000; Rohrer 2018).”
BackgroundThe registration continuum in clinical science: A guide toward transparent practices
Benning, S. D., Bachrach, R. L., Smith, E. A., Freeman, A. J., & Wright, A. G. C. (2019) · Journal of Abnormal Psychology · doi:10.1037/abn0000451 · Cited by 77 · see citing works
“To the extent that researchers view them as nuisance variables, confounds may be treated as covariates in a model to filter out extraneous variability in scores (Rohrer, 2018).”
BackgroundPrecise Answers to Vague Questions: Issues With Interactions
Rohrer, J. M., & Arslan, R. C. (2021) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459211007368 · Cited by 90 · see citing works
Self-citationQuantifying Causal Pathways of Teleconnections
Kretschmer, M., Adams, S. V., Arribas, A., Prudden, R., Robinson, N., Saggioro, E., & Shepherd, T. G. (2021) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-20-0117.1 · Cited by 88 · see citing works
“1999), psychology (Rohrer 2018), and medical research (Richens et al.”
BackgroundMunakata, Y., & Michaelson, L. (2021) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-121318-085005 · Cited by 98 · see citing works
Why most psychological research findings are not even wrong
Scheel, A. M. (2022) · Infant and Child Development · doi:10.1002/icd.2295 · Cited by 91 · see citing works
The Challenge of Generating Causal Hypotheses Using Network Models
Ryan, O., Bringmann, L. F., & Schuurman, N. K. (2022) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2022.2056039 · Cited by 85 · see citing works
“X j denotes that an intervention to change the value of X i will lead to a change in the probability distribution of X j (for an accessible, more elaborate introduction to the use of DAGs in causal inference see, amongst others Hernan & Robins, 2010; Pearl et al., 2016; Rohrer, 2018).”
BackgroundTemperament and brain networks of attention
Posner, M. I., & Rothbart, M. K. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0254 · Cited by 87 · see citing works
van Kessel, R., Wong, B. L. H., Rubinić, I., O’Nuallain, E., & Czabanowska, K. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000013 · Cited by 84 · see citing works
“Even though the study findings were based on directed acyclic graphs to infer causality [37], this study remains cross-sectional in nature and true causality cannot be determined.”
ResultShaver, J. M. (2019) · Journal of Management · doi:10.1177/0149206319846272 · Cited by 84 · see citing works
“This includes fields like economics (e.g., Angrist & Pischke, 2010), political science (e.g., Samii, 2016), sociology (e.g., Gangl, 2010), and psychology (e.g., Rohrer, 2018).”
BackgroundLühr, M., Pavlova, M. K., & Luhmann, M. (2021) · Journal of Happiness Studies · doi:10.1007/s10902-021-00480-4 · Cited by 82 · see citing works
“A usual approach to remove such spurious relationships in regression analyses is to include confounders as covariates in regression models (Rohrer, 2018).”
BackgroundPhase angle as a marker of muscle quality: A systematic review and meta-analysis
Pereira, J. P. D. C., Rebouças, A. D. S., Prado, C. M. M., González, M. C., Cabral, P. C., Diniz, A. D. S., Fayh, A. P. T., & Silva, F. M. (2024) · Clinical Nutrition · doi:10.1016/j.clnu.2024.11.008 · Cited by 87 · see citing works
Causal discovery and the problem of psychological interventions
Eronen, M. I. (2020) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2020.100785 · Cited by 102 · 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.,…”
BackgroundMethodHiwar, W., King, M., Shuweihdi, F., Fletcher, L. A., Dancer, S. J., & Noakes, C. J. (2021) · Indoor Air · doi:10.1111/ina.12846 · Cited by 77 · see citing works
Moral values predict county-level COVID-19 vaccination rates in the United States.
Reimer, N. K., Atari, M., Karimi-Malekabadi, F., Trager, J. P., Kennedy, B., Graham, J., & Dehghani, M. (2022) · American Psychologist · doi:10.1037/amp0001020 · Cited by 71 · see citing works
Foundations and Future Directions for Causal Inference in Ecological Research
Siegel, K., & Dee, L. E. (2025) · Ecology Letters · doi:10.1111/ele.70053 · Cited by 73 · see citing works
Future Directions in the Study and Treatment of Parent–Child Separation
Humphreys, K. L. (2018) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2018.1534209 · Cited by 73 · see citing works
“…variables, such practices are likely to be problematic when the “ covariates ” are actually mechanisms (e.g., covarying for reported distress following separation in examining the association between parent – child separation and subsequent psychopathology; see Kraemer, 2015; Rohrer, 2018).”
BackgroundInfluentialMader, N., Arslan, R. C., Schmukle, S. C., & Rohrer, J. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2212154120 · Cited by 71 · see citing works
“Here, the observed mean level of negative emotion is an outcome of differences in emotional varia-bility—thus, statistical adjustment for it may even induce over-control bias (14) and move estimates away from the true association between neuroticism and emotional variability.”
Self-citationCHIELD: the causal hypotheses in evolutionary linguistics database
Roberts, S. G., Killin, A., Deb, A., Sheard, C., Greenhill, S. J., Sinnemäki, K. T.-K., Segovia‐Martín, J., Nölle, J., Berdičevskis, A., Humphreys-Balkwill, A., et al. (2020) · Journal of Language Evolution · doi:10.1093/jole/lzaa001 · Cited by 70 · see citing works
“Causal inference is an approach to thinking about causality (Pearl, 2000; Pearl & Mackenzie, 2018; Rohrer, 2018) that uses graphical tools - causal graphs - to depict models of causal processes.”
BackgroundMethodInfluentialMichaelson, L. E., & Munakata, Y. (2020) · Psychological Science · doi:10.1177/0956797619896270 · Cited by 69 · see citing works
Bartram, D. (2021) · Social Indicators Research · doi:10.1007/s11205-020-02586-3 · Cited by 68 · see citing works
“Inclusion of intervening variables, in contrast, will introduce/ exacerbate bias (sometimes called “overcontrol bias”, e.g. Rohrer 2018, or “included variable bias”, York 2018).”
BackgroundThe APC-barrier and its effect on stratification in open access publishing
Klebel, T., & Ross‐Hellauer, T. (2023) · Quantitative Science Studies · doi:10.1162/qss_a_00245 · Cited by 85 · see citing works
“DAGs allow us to represent the causal assumptions of the studied phenomenon visually (Rohrer, 2018).”
BackgroundBehaviour Genetic Frameworks of Causal Reasoning for Personality Psychology
Briley, D. A., Livengood, J., & Derringer, J. L. (2018) · European Journal of Personality · doi:10.1002/per.2153 · Cited by 65 · see citing works
“However, several causal models may generate data whereby conscientiousness and life outcomes are correlated (Rohrer, 2018).”
BackgroundBertin, P., Nera, K., Hamer, K., Uhl-Haedicke, I., & Delouvée, S. (2021) · Group Processes & Intergroup Relations · doi:10.1177/1368430221992114 · Cited by 58 · see citing works
“Lastly, causality in the theoretical models should also be carefully defined, as controlling for a variable causally affected by the independent variable can not only result in spurious correlations, but also in the suppression of the effect under investigation (Rohrer, 2018).”
BackgroundPutting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction
Rocca, R., & Yarkoni, T. (2021) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459211026864 · Cited by 59 · see citing works
“…standard regression methods—for example, treating regression coefficients as causal estimates without disclosing and defending causal assumptions (Rohrer, 2018; Watts, 2014; Watts et al., 2018) or concluding that a construct of interest has incremental validity over and above other constructs…”
MethodFoster, C. F. (2023) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2023.2210063 · Cited by 62 · see citing works
“Interpretation is often perceived to be the most challenging aspect of quantitative research (Abelson 2012), and interpretation of quantitative data involves much more than merely distinguishing correlation from causation (see Rohrer 2018).”
BackgroundBridging maladaptive social self-beliefs and social anxiety: a network perspective
Heeren, A., Bernstein, E. E., & McNally, R. J. (2020) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2020.102267 · Cited by 59 · see citing works
“Fourth, one cannot exclude the possibility that some of the non-zero edges in both GGM and DAG network models were artifacts due to conditioning on a collider variable (e.g., variables B in A → B ⟵ C; Rohrer, 2018).”
BackgroundKonuk, F. A., & Otterbring, T. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103531 · Cited by 64 · see citing works
Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science
Radford, J., & Joseph, K. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00018 · Cited by 58 · see citing works
Control variables and causal inference: a question of balance
York, R. (2018) · International Journal of Social Research Methodology · doi:10.1080/13645579.2018.1468730 · Cited by 62 · see citing works
“…and there are no other confounding factors.1 More complicated structures such as having more than three independent variables or complex causal pathways will only make the issues I raise here more problematic (Elwert & Winship, 2014; Morgan & Winship, 2015; Pearl et al., 2016; Rohrer, 2018).”
BackgroundMethodObsessive–compulsive symptoms in eating disorders: A network investigation
Meier, M., Kossakowski, J. J., Jones, P. J., Kay, B., Riemann, B. C., & McNally, R. J. (2019) · International Journal of Eating Disorders · doi:10.1002/eat.23196 · Cited by 61 · see citing works
Su, Y., Hu, M., Wang, Y., Zhang, H., He, C., Wang, Y., Wang, D., Wu, X., Zhuang, Y., Hong, S., et al. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.118759 · Cited by 58 · see citing works
Power to Detect What? Considerations for Planning and Evaluating Sample SizePreprint
Giner‐Sorolla, R., Montoya, A. K., Aberson, C. L., Carpenter, T., Lewis, N. A., Bostyn, D. H., Conrique, B. G., Ng, B., Reifman, A. S., Schoemann, A. M., et al. (2023) · doi:10.31234/osf.io/rv3kw · Cited by 62 · see citing works
Garjani, A., Middleton, R., Hunter, R., Tuite-Dalton, K. A., Coles, A. J., Dobson, R., Duddy, M., Hughes, S., Pearson, O. R., Rog, D. J., et al. (2021) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2021.102939 · Cited by 57 · see citing works
“B.1 and Appendix B) (Greenland et al., 1999; Rohrer, 2018).”
Causal inference on human behaviour
Bailey, D. H., Jung, A. J., Beltz, A. M., Eronen, M. I., Gische, C., Hamaker, E. L., Kording, K. P., Lebel, C., Lindquist, M. A., Moeller, J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01939-z · Cited by 56 · see citing works
“Tools that help with such decision-making processes are increasingly available 7 .”
Self-citationKorhonen, V. A., Mattsson, M. T., Inkinen, M., & Toom, A. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01056 · Cited by 61 · see citing works
“Accordingly, we interpret the study findings as causal hypotheses, while bearing in mind that (1) conditioning on a common effect of two variables may introduce a spurious edge into the graph (Epskamp et al., 2018; Rohrer, 2018), (2) a clique in the network graph may indicate the presence of an unmodeled latent variab…”
BackgroundMethodInfluentialScorpion species of medical importance in the Brazilian Amazon: a review to identify knowledge gaps
Martins, J. G., Santos, G. C., Procópio, R. E. D. L., Arantes, E. C., & Bordon, K. D. C. F. (2021) · The Journal of venomous animals and toxins including tropical diseases · doi:10.1590/1678-9199-jvatitd-2021-0012 · Cited by 55 · see citing works
“Because correlation does not imply causation [157], an experimental randomized trial would be required in order to support a conclusion of cause and effect.”
BackgroundAge-of-acquisition effects: A literature review.
Elsherif, M. M., Preece, E., & Catling, J. C. (2023) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0001215 · Cited by 55 · see citing works
Goldan, J., Nusser, L., & Gebel, M. (2022) · Child Indicators Research · doi:10.1007/s12187-022-09914-8 · Cited by 54 · see citing works
“To avoid overcontrol bias, we do not control for any mediating variables in the causal relationship between SEN status and schoolrelated SWB outcome variables because our interest is in the gross effect (Elwert & Winship, 2014; Rohrer, 2018).”
BackgroundMethodHoogeveen, S., Haaf, J. M., Bulbulia, J. A., Ross, R., McKay, R. T., Altay, S., Bendixen, T., Berniūnas, R., Cheshin, A., Gentili, C., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01273-8 · Cited by 67 · see citing works
A review of machine learning with small and limited data
Naser, M. Z. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01346-9 · Cited by 80 · see citing works
“Recognizing such limitations, recent work has aimed to advance causal inference to uncover the data distribution and map the underlying causal graph in synergy with small data [63, 64].”
Individual differences in mathematical cognition: a Bert's eye view
Smedt, B. D. (2022) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2022.101175 · Cited by 52 · see citing works
A correlation network analysis of dissociative experiences
Schimmenti, A., & Şar, V. (2019) · Journal of Trauma & Dissociation · doi:10.1080/15299732.2019.1572045 · Cited by 50 · see citing works
“Furthermore, common endogenous selection biases, such as nonresponse, attrition, and representativity bias (see Rohrer, 2018, for a review) would not apply to the current study only if it is correctly considered as a preliminary investigation on the cognitive and emotional dissociative processes…”
BackgroundInformation flow-based fuzzy cognitive maps with enhanced interpretability
Tyrovolas, M., Liang, X. S., & Stylios, C. D. (2023) · Granular Computing · doi:10.1007/s41066-023-00417-7 · Cited by 49 · see citing works
“Similarly, relying on the correlation coefficient is equally unreliable because it has been shown that correlation does not necessarily imply causality (Rohrer 2018).”
BackgroundReconsidering evidence of moral contagion in online social networks
Burton, J. W., Cruz, N., & Hahn, U. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01133-5 · Cited by 42 · see citing works
Trauma exposure and eating disorders: Results from a United States nationally representative sample
Convertino, A. D., Morland, L. A., & Blashill, A. J. (2022) · International Journal of Eating Disorders · doi:10.1002/eat.23757 · Cited by 49 · see citing works
“…(i.e., variables that may connote a common cause for both the independent variable and outcome) were controlled for, but collider variables (i.e., variables that are caused by both the independent variable and outcome) and mediator variables were not (see Rohrer, 2018 for further explanation).”
BackgroundLocal acceptance of solar farms: The impact of energy narratives
Scovell, M., McCrea, R., Walton, A., & Poruschi, L. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114029 · Cited by 48 · see citing works
- Authors
- Rohrer, J. M.
- Affiliations
- German Institute for Economic Research (DE)
- Corresponding author
- Rohrer, Julia M.
- ORCID
- Rohrer, Julia M.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2018-01-29
- Volume
- 1
- Issue
- 1
- Pages
- 27-42
- ISSN
- 2515-2459 (ISSN-L)
- 2515-2467
- OpenAlex
- W2776946813
- Cited by
- 1,002
- References
- 47
- Citations by year
- 2026: 124 · 2025: 145 · 2024: 150 · 2023: 147 · 2022: 122 · 2021: 132 · 2020: 137 · 2019: 31 · 2018: 14
- FWCI
- 65.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Advances in Methods and Practices in Psychological Science (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- causal inference, observational data, causal graphical models, confounding, third variable, particle colliders, mediation, statistical control, causal assumptions, correlation versus causation
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