How can I evaluate the balance of Covariates after CausalForestDML?
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- jupyter-notebook
- Domain
- machine-learning
Research direction
Begin with the CausalForestDML entry point and the AACC and T_residual concepts named in the issue. Compare the estimator's documented behavior with balance diagnostics for weighted or matching methods; done means a project-backed explanation of whether the proposed diagnostic is appropriate.
Written by the indexing model from the issue text.
Description
In the continuous settings, it is normal to calculate the AACC between T and X, so we can know the balance of covariates after the "process".
But, how can I evaluate the balance after CausalForestDML, doesn't like weighted methods or matching methods, it seemed that causalforest didn't do anything to the samples, it just output the marginal treatment effect of every sample.
In this case, is it reasonable to calculate the AACC between T_residual and X?
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