Process optimization and innovation are essential in a competitive and digitalized industry driven by the Quality-by-Design paradigm. This requires building a causal model that explains how variations in the inputs relate to variations in the outputs. Traditionally, deterministic models are preferred for this purpose, but these are often unfeasible due to limited knowledge and high development costs. As a result, data-driven models are increasingly used. To maintain causality in these models, independent input variation is necessary—typically achieved through Design of Experiments (DOE). However, in Industry 4.0 contexts, performing DOE is challenging due to complex variable correlations and the high number of factors involved. Although large volumes of production data are available, they often lack the independence required for causal inference, making traditional statistical and machine learning models ineffective for optimization. Consequently, there is growing interest in developing causal models from historical data. This paper explores two promising methods: retrospective DOE and causal latent space-based modeling.