hal9001
lmtp
| hal9001 | lmtp | |
|---|---|---|
| 1 | 1 | |
| 48 | 53 | |
| - | - | |
| 5.2 | 4.9 | |
| 15 days ago | 3 days ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU Affero General Public License v3.0 |
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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
lmtp
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[Q] Should G-methods, IPTW always be used over traditional regression?
The tlverse/sl3 super learner library is much better integrated and a lot more powerful (a bit more complicated in the beginning but once you understand it, its great). LMTP has a separate branch that uses sl3: https://github.com/nt-williams/lmtp/tree/sl3-devel. To specify formulas is sl3, you just do Lrnr_glmnet$new(formula = ~ 1 + W + A + A*W), but make sure to download the "dev" version: devtools::install_github("tlverse/sl3", ref = "devel").
What are some alternatives?
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
yaglm - A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
tmle3mopttx - 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
sjPlot - sjPlot - Data Visualization for Statistics in Social Science
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
MicrobiomeStat - Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime
vip - Variable Importance Plots (VIPs)
hermiter - Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)