hal9001
yaglm
| hal9001 | yaglm | |
|---|---|---|
| 1 | 1 | |
| 48 | 53 | |
| - | - | |
| 5.2 | 0.0 | |
| 15 days ago | about 1 year ago | |
| R | Python | |
| GNU General Public License v3.0 only | MIT License |
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hal9001
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[Q] Should G-methods, IPTW always be used over traditional regression?
Another approach is to make your own SL learner. It turns out to be not as difficult as it may seem to do this. You still pass in the same character string to the SuperLearner functions (e.g. "SL.customlearner") and it will extract the function "SL.customlearner" from your R environment. Here is one example: https://github.com/tlverse/hal9001/blob/devel/R/sl_hal9001.R
yaglm
What are some alternatives?
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
scikit-learn - scikit-learn: machine learning in Python
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime