Comments (1)
thanks for the feedback. As double machine learning is a framework that mainly focuses on estimation and inference of causal effects we put emphasis on providing these methods in our implementation in Python and R. In the future we may add some prediction functionalities as well.
Once more, thank you.
Best,
Philipp
from doubleml-for-py.
Related Issues (20)
- Failing Jobs for Python 3.6. HOT 1
- [Bug]: KeyError in DoubleMLPLIV.fit() with multiple instruments and store_predictions=True HOT 1
- [Bug]: RMSE calculation and nuisance evaluation for IRM models does not account for conditional observations
- [Feature Request]: Add Clustering for non-linear DML models HOT 1
- [Feature Request]: Panel data and individual effect control HOT 7
- CATE with continuous treatment and categorical outcome HOT 5
- [Bug]: Multi-treatment data creation bug HOT 2
- [Feature Request]: Request for individual scores from IRM model HOT 2
- [Feature Request]: Expose last stage score function estimation HOT 4
- [Feature Request]: Add Weights to IRM Model for Policy Evaluation HOT 3
- [Bug]: type casting outcome_variable and treatment_variable(s) HOT 1
- Add support for binary outcome in PLR
- [Bug]: HOT 2
- [Feature Request]: Support for multiple discrete treatments in `doubleml.DoubleMLIRM` HOT 3
- [Feature Request]: DoubleMPLR weights support HOT 4
- [Feature Request]: CATE for instrument variable based models HOT 1
- [Feature Request]: Overlap diagnostics for propensity scores
- [Feature Request]: Enable CATE and GATE for multiple repetitions
- [Feature Request]: Abstract DoubleMLBase class
- Rework the DID Sensitvitiy
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