The untapped potential of causal inference in cross-modal research

Cross-modal effects have recently become a popular topic in building science. However, studies in this area frequently neglect causal inference, leading to a lack of valid causal results. To address this problem, we specifically highlight causality and its importance to cross-modal research. We present three general guidelines, and describe them using toy examples, for appropriately conducting causal cross-modal research. The guidelines originate from the methodological framework for quantitative social science by Lundberg et al. (2021). They are as follows: i) specify the theoretical estimand as the target of causal inference; ii) specify the empirical estimand that is informative for the theoretical estimand based on causal assumptions; iii) select the estimation strategy empirically to estimate the empirical estimand. In light of these guidelines, we discuss some common methodological pitfalls in current research practices that can jeopardize causal inference. Moreover, we offer certain recommendations to avoid such pitfalls. The general objective of this paper is to promote transparent causal cross-modal research by raising the awareness of causal inference in view of appropriate causality-related methodological choices.