Treating symptoms or root causes: How does information about causal mechanisms affect interventions?

2026-01-27

AI Summary

When confronted with complex problems, individuals often favor direct interventions that merely alleviate symptoms over indirect interventions targeting root causes. This study employs cognitive psychology experiments in a hoof-trimming context to systematically investigate whether the level of detail in causal mechanism information and its presentation format—as causal diagrams within multifactorial scenarios—enhances acceptance of indirect interventions. Results indicate that although providing mechanistic information slightly reduced skepticism toward indirect approaches, it did not significantly shift the prevailing preference for symptomatic treatment. These findings challenge the common assumption that merely supplying causal mechanisms promotes rational decision-making, revealing for the first time that such information alone is insufficient to foster choices favoring sustainable interventions.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

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.

Problem

• causal mechanisms
• intervention choice
• symptom vs. root cause
• decision making
• complex problem solving

Innovation

• causal mechanisms
• intervention preference
• causal diagrams
• root cause vs. symptom
• mechanistic reasoning