causalglm
drake
| causalglm | drake | |
|---|---|---|
| 2 | 1 | |
| 17 | 1,330 | |
| - | 0.1% | |
| 0.0 | 6.1 | |
| about 2 years ago | about 2 months ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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causalglm
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[Q] Sensitivity of (Causal) Inference to Nonlinear Functional Form
Why not both? https://tlverse.org/causalglm/ (Will replace this with a more informative comment when I have free time later today)
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[Q] Should G-methods, IPTW always be used over traditional regression?
This package: https://github.com/tlverse/causalglm was recently developed to fill the gap between fully black box causal learning methods for heterogeneous treatment effects and fully parametric generalized linear model approaches. It allows for both semiparametric and nonparametric robust causal inference for user defined “working parametric models” for the estimands of interest. It is still black box in that non relevant features of the data distribution are estimated using machine learning but the relevant conditional parameters are modeled fully parametrically (with nonparametric robust inference when misspecified). It is very new so use with caution.
drake
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Your impression of {targets}? (r package)
The targets package is the official successor to Drake, and has the same primary author (Will Landau). He has explained why he created targets, which includes stronger guardrails for users and better UX.
What are some alternatives?
EconML - ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
targets - Function-oriented Make-like declarative workflows for R
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
easystats - :milky_way: The R easystats-project
mlr3learners - Recommended learners for mlr3
tabulapdf - Bindings for Tabula PDF Table Extractor Library
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
ncaahoopR - An R package for working with NCAA Basketball Play-by-Play Data
looper - A resource list for causality in statistics, data science and physics
fiery - A flexible and lightweight web server
tmle3mopttx - 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention
droll - An R package to analyze roll distributions