Many factors in psychology can not be manipulated, but their effects on outcomes must be assessed to inform theories and to investigate potential effects of interventions. Avoiding causality has [harmed science][1] and this goes on although the methodical toolbox for addressing causal questions has been [largely enriched][2] during the last decades. However, causal analyses with transparent assumptions are still very rare in psychology. Instead, causal conclusions have been shown to be [more readily][3] drawn when a paper **avoids** expliciting causality. This has been [explained by a model][4] that assumes that researchers, if methodologically challenged, defend their results as being only associations. However, a causal interpretation must be added to give the results relevant meaning. We have conducted online study that explores the potential effects of triggering the conflict between the common causal avoidance in "correlation is not causation" and the need for a causal assessment. The study investigated whether this affects the motivation to address causality explicitly and stances towards the issue. We compared three randomized groups (controls, explicit conflict, explicit conflict + explicit potential benefits of addressing the topic). We have pre-registered material, hypotheses, some explorative plans and the associated analyses (just here in unstandardized format). In the same way, the sampling scheme had been pre-registered (stated on July 12th, 2021). [1]: https://ajph.aphapublications.org/doi/10.2105/AJPH.2018.304337 [2]: https://cpe.psychopen.eu/index.php/cpe/article/download/3873/3873.html?inline=1 [3]: https://psyarxiv.com/nkf96/ [4]: https://psyarxiv.com/8hr7n/