Comments (3)
For all the interference functionality #15 #14 #29 will need to have NetworkX somewhere in the background. Since not everyone uses methods to handle interference, I would prefer to have NetworkX as an extra. For adding extras, see https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras-optional-features-with-their-own-dependencies
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Before doing this, I have to figure out stochastic interventions as described by van der Laan.
Also will need to rewrite TMLE slightly. For stochastic interventions, need to have iterative process of Qn fitting (unlike the present TMLE which converges in a single step)
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Due to various other requirements (statsmodels 0.10.0) and the less broad use-case for interference methods, this will become its own library. This applies to all interference causal inference methods
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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
- MonteCarloGFormula
- Add Odds Ratio and other estimands for AIPTW and TMLE
- Addition of meta-analysis tools
- 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
- Perfect separation error for using `SingleCrossfitTMLE` HOT 2
- 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
- Risk Ratio Summary HOT 1
- The default regression argument of zepid.base.interaction_contrast_ratio differs from the description in the documentation. HOT 4
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