Structural Determinants of Interventions on Causal Systems

We investigate how people use causal knowledge to design interventions to affect the outcomes of causal systems. We propose that in addition to using content or mechanism knowledge to evaluate the effectiveness of interventions, people are also influenced by the abstract structural properties of a causal system. In particular, we investigated two factors that influence whether people tend to intervene proximally (on the immediate cause of an outcome of interest) or distally (on the root cause of a chain leading to the outcome). We presented people with causal chains describing a variety of real-world and artificial causal systems and asked them where they would intervene to affect the outcome. In Experiment 1, participants who were asked to choose the best long-term intervention intervened more distally than participants asked to choose the best short-term intervention. In Experiment 2, participants presented with a branching structure in which there were two distinct causal pathways from the root cause to the outcome were more likely to intervene on the root cause than participants presented with only one of the pathways. Our findings demonstrate two ways in which people integrate content knowledge and knowledge of a system’s causal structure to design effective interventions.