How is Causal Inference Different in Academia and Industry? | by Zijing Zhu, PhD | Jan, 2024

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A Bonus Article for “The Book of Why” Series

Zijing Zhu, PhD

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

Inspired by my own background as an academic researcher who studied causal inference in economics during my Ph.D. program, as well as my as a in the industry building causal models to make forecasts, for the bonus article, I would like to share my understanding of the concept of causal inference and the similarities and differences in how it is applied in academic and industry settings.

Due to the difference in the nature and of academic and industry applications, the causal inference workflows are quite different between the two.

Speed

Academic research usually operates at a slower pace, from forming ideas to drawing final conclusions. It focuses on building not only on the causal conclusion itself but also on the data involved, methods used, and robustness of the research. Thus, oftentimes, the research process is extended to validate data eligibility, run sensitivity analysis, test causal structures, etc.

However, for business, time is money. Tech firms are more practical. They would rather focus their resources on building scalable applications that can be put into production and bring benefits quickly. The cost of waiting for a perfect and generalizable model is high. Thus, the industry would prefer to have a benchmark model available as a placeholder first before -tuning and making adjustments.

Photo by Veri Ivanova on Unsplash

Method

Indeed, academic research is the of new approaches and mechanisms for theoretical . However, empirical researchers who focus on observational studies or experiments tend to use standard and well-established methodologies. For example, Difference-in-Differences (DID)…

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