tmle3mopttx
causalglm
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| tmle3mopttx | causalglm | |
|---|---|---|
| 1 | 2 | |
| 10 | 17 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | about 2 years ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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tmle3mopttx
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[D] Is there a such thing as "Prespective Statistical Models"?
This package and the references therein allows for nonparametric estimation and inference for the optimal dynamic treatment: https://github.com/tlverse/tmle3mopttx.
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.
What are some alternatives?
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
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.
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looper - A resource list for causality in statistics, data science and physics
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mlr3 - mlr3: Machine Learning in R - next generation