Using causal inference to avoid fallouts in data-driven parametric
  analysis: a case study in the architecture, engineering, and construction
  industry
Xia Chen Ruiji Sun Ueli Saluz Stefano Schiavon Philipp Geyer
The decision-making process in real-world implementations has been affected
by a growing reliance on data-driven models. We investigated the synergetic
pattern between the data-driven methods, empirical domain knowledge, and
first-principles simulations. We showed the potential risk of biased results
when using data-driven models without causal analysis. Using a case study
assessing the implication of several design solutions on the energy consumption
of a building, we proved the necessity of causal analysis during the
data-driven modeling process. We concluded that: (a) Data-driven models'
accuracy assessment or domain knowledge screening may not rule out biased and
spurious results; (b) Data-driven models' feature selection should involve
careful consideration of causal relationships, especially colliders; (c) Causal
analysis results can be used as an aid to first-principles simulation design
and parameter checking to avoid cognitive biases. We proved the benefits of
causal analysis when applied to data-driven models in building engineering.