Summary of Double Machine Learning at Scale to Predict Causal Impact Of Customer Actions, by Sushant More et al.
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Econometrics (econ.EM); Applications (stat.AP); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops a novel approach to estimating the causal impact (CI) of customer actions on business decisions. The authors apply double machine learning (DML) methodology to estimate CI values across hundreds of customer actions and millions of customers. They operationalize DML through a causal ML library based on Spark, allowing for flexible model configurations and scalability. The paper outlines the DML methodology and implementation, highlighting benefits over traditional potential outcomes-based CI models. The authors provide population-level and customer-level CI values with confidence intervals, demonstrating improved accuracy (2.2% gain) and computational efficiency (2.5X faster). This contribution advances the scalable application of CI, providing an interface for faster experimentation, cross-platform support, and improved accessibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how customers’ actions affect business decisions. It uses a new way to calculate this effect called double machine learning (DML). The authors developed a special library that makes it easier to use DML with big data. They tested their method on many customer actions and millions of people, showing that it’s more accurate than other methods. Their work makes it faster and easier for businesses to understand how customers’ actions affect them. |
Keywords
* Artificial intelligence * Machine learning
