Can Digital Soil Mapping Be Causal?

ABSTRACT All too often, it is unclear whether digital soil mapping (DSM) models can support causal interpretation. A common practice in DSM studies is to interpret the importance of covariates for prediction. This carries an implicit causal assumption that is rarely stated and even more rarely justified. Because DSM relies entirely on observational data, it is widely assumed that causal inference is not possible. But is it? Here, we discuss the conditions under which causal inference with observational data is possible and two views of causality. We show that while under each of the views causal inference may be possible, a so‐called generative view is the one most capable of satisfying the conditions for causal inference in DSM. Generative causality treats causation as the system of processes that produce observed associations, rather than relying on associations themselves, as is common in current DSM studies. Realizing this perspective requires DSM to shift towards models in which soil‐forming factors influence soil properties through explicitly modelled processes, which some would call process‐informed DSM. Since these processes are ‘fully determined’ by the modeller's specification, they offer a structured means to control confounding and open the door to applying existing causal inference frameworks. While generative DSM is formally possible, we should ultimately ask whether causal inference ought to be a primary goal, since the primary strength of DSM lies not in establishing causality but in delivering accurate predictions and highlighting patterns that warrant further investigation.