-
- [x] Task 1a: Explore current data
- [x] TUS (public) (MVP)
- [ ] NTS (private, available but needs understanding for Tech Plat use) (for future)
- [ ] Task 1b: Add activity chains to populat…
-
Hello. I would like to create an uplift model to prioritize the best customers to contact. Since there is observational data available, I prefer to go that way as it's less time consuming than setting…
NHUV updated
9 months ago
-
Thanks so much for this fantastic and really helpful package!
In the documentation it is recommended that for Cox regression models,` coxph()` (survival package) should be used after weighting and …
-
Hello,
when reading documentations about Propensity Score Matching, mostly only one Regression formula is observed, wherein Matching, etc. is conducted.
In my case, I have four different Hypothese…
-
I have dataset with up to 400,000 entries and I'm using MatchIt to match 2 groups. It's very slow and I tried different ways (Parallel Processing...) to speed up the process but I couldn't see the mat…
-
I came across an ERROR every time I tried to use 'exact=' while running **optimal matching** (by switching to nearest neighbor matching or having exact=null, the error went away). The R code and error…
-
The way PsmPy computes balanced propensity is really odd. For this reason we may want to have part of their pipelines with our propensity scores.
Optional because I now consider psmpy as a bit brok…
-
**Submitting author:** @ldliao (Lauren Liao)
**Repository:** https://github.com/ldliao/jointVIP
**Branch with paper.md** (empty if default branch):
**Version:** 0.1.2
**Editor:** @ajstewartlang
**Rev…
-
Hello,
I discovered that Matchit does not randomize the data. In the example script, I am using covariate AGE to predict IS_CASE. However, the algorithm picks the first occurrence from each group r…
-
Hello,
in the Matchit-guide and in other associated literature, it is suggested that one should use Outcome Predictors and True Confounders (with impact on both treatment and outcome variable) for …