Comments (1)
Yeah focusing on balancing the covariates seems like a good strategy for the IPTW approach. The paper you linked looks like a good start. I will have to read more about the continuous treatments in that paper (since I am still not clear how continuous treatments operate in a IPW framework)
One of the students at UNC talked about an R package he has been working on dealing with balance for propensity scores. It does some interesting things like setting the maximum allowable unbalance to exist. You can vary this threshold to see if you have any gains in efficiency relative to allowing more unbalance
https://github.com/ngreifer/optweight
It uses a quadratic solver for the optimization problem. The interesting item to me is the longitudinal optimal weights, since that is a slightly harder problem. Since this is more complex (and a little outside my experience since I haven't used quadratic solvers), the paper you linked is probably the better start
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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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