Correlation to causality

ABSTRACT

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.

Acknowledgments

The authors would like to extend their gratitude to Dr. Anil Menon and Dr. Bo Friis Nielsen for their corrections and comments on the initial draft of this Quality Quandaries.

Additional information

Notes on contributors

Marta Rotari

Marta Rotari received her PhD in Statistics and Data Analysis from the Technical University of Denmark. Her research interests include statistical process control, statistical modeling, and supervised and unsupervised machine learning methods.

Murat Kulahci

Murat Kulahci is a professor at the Technical University of Denmark and Luleå University of Technology in Sweden. His research currently focuses primarily on large data analytics for descriptive, inferential and predictive purposes. Many of his research applications involve high dimensional, high frequency data demanding analysis methods in chemometrics and machine learning. He has been collaborating with various industries in many industrial statistics projects and digital manufacturing.