Treating symptoms or root causes: How does information about causal mechanisms affect interventions?
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908793140412416 |
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| author | Müller, Romy |
| author_facet | Müller, Romy |
| contents | When deciding how to solve complex problems, it seems important not only to know whether an intervention is helpful but also to understand why. Therefore, the present study investigated whether explicit information about causal mechanisms enables people to distinguish between multiple interventions. It was hypothesised that mechanism information helps them appreciate indirect interventions that treat the root causes of a problem instead of just fixing its symptoms. This was investigated in an experimental hoof trimming scenario in which participants evaluated various interventions. To do so, they received causal diagrams with different types of causal information and levels of mechanistic detail. While detailed mechanism information and its embedding in the context of other influences made participants less sceptical towards indirect interventions, the effects were quite small. Moreover, it did not mitigate participants' robust preference for interventions that only fix a problem's symptoms. Taken together, the findings suggest that in order to help people choose sustainable interventions, it is not sufficient to make information about causal mechanisms available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20010 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Treating symptoms or root causes: How does information about causal mechanisms affect interventions? Müller, Romy Human-Computer Interaction When deciding how to solve complex problems, it seems important not only to know whether an intervention is helpful but also to understand why. Therefore, the present study investigated whether explicit information about causal mechanisms enables people to distinguish between multiple interventions. It was hypothesised that mechanism information helps them appreciate indirect interventions that treat the root causes of a problem instead of just fixing its symptoms. This was investigated in an experimental hoof trimming scenario in which participants evaluated various interventions. To do so, they received causal diagrams with different types of causal information and levels of mechanistic detail. While detailed mechanism information and its embedding in the context of other influences made participants less sceptical towards indirect interventions, the effects were quite small. Moreover, it did not mitigate participants' robust preference for interventions that only fix a problem's symptoms. Taken together, the findings suggest that in order to help people choose sustainable interventions, it is not sufficient to make information about causal mechanisms available. |
| title | Treating symptoms or root causes: How does information about causal mechanisms affect interventions? |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.20010 |