yaglm
hal9001
Our great sponsors
| yaglm | hal9001 | |
|---|---|---|
| 1 | 1 | |
| 53 | 48 | |
| - | - | |
| 0.0 | 5.2 | |
| about 1 year ago | 10 days ago | |
| Python | R | |
| MIT License | GNU General Public License v3.0 only |
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yaglm
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
What are some alternatives?
ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
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:
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime