True TE estimates in customer segmentation example
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Description
Hi, I have a couple of questions for Case Study - Customer Segmentation at An Online Media Company
- In the true te estimate part, why was it not just gamma_fn(X) but instead price*gamma_fn(X)/demand? Because I see the formula in DGP is Y = gamma(X).T + beta(X,W), then I presume gamma(X) is the CATE.
- How should we actually interpret the CATE(X) when the Y and T were log transformed? As I understand, The CATE(X) interpretation when using raw Y and T is for example CATE 0.3 means that on certain X value, the Y will change by 0.3 with the change in treatment. I'm not sure with the log transformation being used here.
I appreciate your responses. Thanks!
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Research direction
Start with the CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb notebook, especially the true TE estimate section and its DGP formula. Review how the notebook defines gamma_fn, price, demand, Y, and T, then clarify the rationale for the estimate and how to interpret CATE after log transformation. Done means the notebook answers both questions clearly.
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Assessment
- Tech stack
- jupyter-notebook
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100