Ein neuer Zugang zu einem bekannten Problem mit normativ aufgeladenen Befragungsthemen1
“Biodiversity” is a particular term that brings complex ideas of the “abundance of life” to a scientific formula and at the same time marks it as a desirable state. Since the early 1990s, the meta-narrative “threatened biodiversity” has functioned as a meaningful moment of international environmental policy. It is well known that the attitude surveys on social awareness of biodiversity, which the “Bundesamt für Naturschutz” (Federal Agency for Nature Conservation) has had conducted every two years since 2009, are contaminated by social desirability. Since previous analyses either explicitly or implicitly assume that the form of bias is uniform, statements regarding associations and group comparisons are at risk. Causal machine learning methods open up a new analytical approach to the underlying statistical problem. They can be used to uncover how causal effects vary and which variables are related to this variation in such a way that they can explain effect differences. In our specific case, we use causal forest models to show that the influence of social desirability on attitude patterns and behavioral intentions about biodiversity varies systematically, with this effect heterogeneity being significantly associated with the age of the interviewees in a non-linear manner. Furthermore, we present this procedure as a methodological innovation for quality assurance in normatively charged survey topics.