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Causality is important in many engineering applications for the optimization, robustness, and control of manufacturing processes. Randomized experiments have been the conventional tool for establishing and quantifying causal effects. With great advances in sensorics and information technology, the temptation to use an unprecedented amount of observational data for this purpose has been growing. Most classically trained data scientists are warned against such practice as jumping from correlation to causality is presented as a treacherous leap. Yet causal inference based on observational data has been of great interest in, for example, social and medical studies for which randomized experiments can be infeasible or even unethical. In this Quality Quandaries, we propose a compromise where observational data, with the help of process expertise, is used to establish the set of factors that will be further tested for causality. We demonstrate a practical application of our proposal through a case study in additive manufacturing. [ABSTRACT FROM AUTHOR]