A Bonus Article for “The Book of Why” Series

In two months, we finished reading “The Book of Why,” which gave us a glimpse into the fascinating world of causality. As promised, I even have a bonus article to shut my first Read with Me series officially.

Inspired by my very own background as a tutorial researcher who studied causal inference in economics during my Ph.D. program, in addition to my experience as an information scientist within the industry constructing causal models to make demand forecasts, for the bonus article, I would really like to share my understanding of the concept of causal inference and the similarities and differences in the way it is applied in academic and industry settings.

On account of the difference in the character and purpose of educational research and industry applications, the causal inference workflows are quite different between the 2.

Speed

Academic research often operates at a slower pace, from forming ideas to drawing final conclusions. It focuses on constructing trust not only on the causal conclusion itself but in addition on the info involved, methods used, and robustness of the research. Thus, oftentimes, the research process is prolonged to validate data eligibility, run sensitivity evaluation, test causal structures, etc.

Nevertheless, for business, time is money. Tech firms are more practical. They might quite focus their resources on constructing scalable applications that might be put into production and produce advantages quickly. The associated fee of waiting for an ideal and generalizable model is high. Thus, the industry would favor to have a benchmark model available as a placeholder first before fine-tuning and making adjustments.

Method

Indeed, academic research is the source of latest approaches and mechanisms for theoretical researchers. Nevertheless, empirical researchers who give attention to observational studies or experiments are likely to use standard and well-established methodologies. For instance, Difference-in-Differences (DID)…