The text discusses the differences and similarities in applying causal inference in academic and industry settings. It highlights differences in workflows, speed, methods, feedback loop, and the importance of Average Treatment Effect (ATE) vs. Individual Treatment Effect (ITE), as well as similarities in assumptions, expert input, and transparency. The article reflects on a 12-week reading series of “The Book of Why.”
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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 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 experience as a data scientist in the industry building causal models to make demand 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.
Differences
Due to the difference in the nature and purpose of academic research 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 trust not only on the causal conclusion itself but also on the data involved, methods used, and robustness of the research. However, for business, time is money. Tech firms are more practical and prefer scalable applications that can bring benefits quickly.
Method
Academic research is the source of new approaches and mechanisms for theoretical researchers. However, industries are always open to cutting-edge techniques and buzzwords.
Feedback Loop
When researchers have some initial results, they will attend conferences or other events to network their findings to gather feedback. When tech companies have a causal model with decent performance, they will launch this application into their business decision-making.
Average Treatment Effect (ATE) vs. Individual Treatment Effect (ITE)
In academics, it is often the case to evaluate ATE for a population or subpopulation. On the contrary, industrial causal applications are more focused on individual-level decision-making.
Model Efficiency
Industry applications sometimes need to be put into production to make real-time business decisions. Thus, we need to consider the run time of the model and its scalability when embedding a causal engine into business decision-making, which is less of a concern for academic research.
The Ending
It seems that academic research can go on forever. On the other hand, industrial applications focus on the key KPI improvements.
Similarities
Despite the differences, there are also many similarities that academic research and industry share.
Assumptions
The world is not perfect. Thus, both research and industry applications start with assumptions.
Expert Input
Both research and industry applications need expert input to draft the causal structure.
Transparency
Both research and industry applications embrace transparency when including causality.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- How is Causal Inference Different in Academia and Industry?
- Towards Data Science – Medium
- Twitter – @itinaicom
