causalglm
looper
| causalglm | looper | |
|---|---|---|
| 2 | 2 | |
| 17 | 235 | |
| - | - | |
| 0.0 | 7.3 | |
| about 2 years ago | about 2 months ago | |
| R | ||
| GNU General Public License v3.0 only | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
causalglm
-
[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)
-
[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.
looper
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.
Data-science-best-resources - Carefully curated resource links for data science in one place
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
mlr3learners - Recommended learners for mlr3
dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
datascience - Curated list of Python resources for data science.
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
tmle3mopttx - 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.