Comments (1)
pzivich
commented on June 15, 2024
TMLE now includes the bound option in the exposure_model function. It defaults to no truncation of the data. Either symmetric or asymmetric truncation can be specified for the predicted probabilities.
Will add Q-model bounding in the future
Will be part of 0.3.2 release
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Related Issues (20)
- IPTW handle PerfectSeparationErrors in the marginal structural model better
- AIPW for survival analysis ? HOT 1
- Dual treatments
- ValueError better pytest strategy
- Package compatibility? HOT 2
- Update documentation (and possibly re-organize) HOT 2
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- add p-value column in a forrest plot/ effectmeasureplot HOT 2
- Enhancement in graphics.py to change odds text size HOT 1
- Saving DAGs programatically HOT 11
- sklearn dependancy in setup.py should be scikit-learn HOT 1
- AIPW formula equivalent to what's in the literature? HOT 2
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- Superlearn check weights HOT 2
- SingleCrossFit `invalid value encountered in log` HOT 8
- Unable to install latest 0.9.0 version through pip HOT 7
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