CausalAnalysis function results for Market access problem [Question]
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Description
Hi, without giving too much information we are using the DML-RF with this CausalAnalysis function to get out point estimates for a given treatment. The outcome is market share and the problem is some point estimates are turning out to be negative but out assumption states that given T=1 or T=0 the treatment can never cause to negative market share, so I'm wondering how can we impose such an assumption? Or is it wrong to impose such an assumption? (this is an assumption from the business team).
Many thanks in advance :)
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Research direction
Start with the CausalAnalysis function and its DML-RF behavior. Determine whether nonnegative market-share point estimates are an expected constraint or a requested capability, then check whether the issue provides enough detail to define the assumption and its validation. Done means the supported behavior or required change is clearly specified.
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Assessment
- Tech stack
- machine-learning, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100