CATE intercept vs ATE
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Description
Hi,
I had a question about how to interpret the CATE intercept beta_t for linearDRLearner. Suppose I fit a model with 3 X's for heterogeneity. Does b_t give me the "average" heterogeneity for these 3 X's, and each individual X coefficient tells me the incremental effect? Somehow I was expecting beta_t to capture the ATE in the absence of heterogeneity but that doesn't seem to be the case (e.g beta_t is not significant although the ATE estimates obtained by setting X=None are, consistent with OLS results)..
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Research direction
Start with the linearDRLearner entry point and its documentation, focusing on the CATE intercept beta_t, the heterogeneity coefficients, and ATE estimates obtained with X=None. Determine whether the documentation explains their relationship; done means the interpretation and the distinction between these estimates are clearly documented.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100