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causalegm

A General Causal Inference Framework by Encoding Generative Modeling

https://github.com/suwonglab/causalegm

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    2 of 4 committers (50.0%) from academic institutions
  • ✓
    Institutional organization owner
    Organization suwonglab has institutional domain (web.stanford.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (14.8%) to scientific vocabulary

Keywords

causal-inference causal-machine-learning causality deep-generative-model generative-model treatment-effects
Last synced: 6 months ago · JSON representation

Repository

A General Causal Inference Framework by Encoding Generative Modeling

Basic Info
Statistics
  • Stars: 73
  • Watchers: 3
  • Forks: 11
  • Open Issues: 1
  • Releases: 2
Topics
causal-inference causal-machine-learning causality deep-generative-model generative-model treatment-effects
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

PyPI CRAN Anaconda Travis (.org) All Platforms Documentation Status

An Encoding Generative Modeling Approach for Dimension Reduction and Covariate Adjustment

CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings.

CausalEGM simultaneously decouples the dependencies of confounders on both treatment and outcome and maps the confounders to the low-dimensional latent space. By conditioning on the low-dimensional latent features, CausalEGM can estimate the causal effect for each individual or the average causal effect within a population.

CausalEGM was originally developed with Python and TensorFlow. Now both Python and R package for CausalEGM are available! Besides, we provide a console program to run CausalEGM directly without running any script. For more information, checkout the Document.

Note that a GPU is recommended for accelerating the model training. However, GPU is not a must, CausalEGM can be installed on any personal computer (e.g, Macbook) or computational cluster with CPU only.

CausalEGM Main Applications

  • Estimate average treatment effect (ATE).

  • Estimate individual treatment effect (ITE).

  • Estiamte average dose response function (ADRF).

  • Estimate conditional average treatment effect (CATE).

  • Built-in simulation and semi-simulation datasets.

Checkout application examples in the Python Tutorial and R Tutorial.

Latest News

  • May/2024: CausalEGM is published online on PNAS.

  • Mar/2023: CausalEGM is available in CRAN as a stand-alone R package.

  • Feb/2023: Version 0.2.6 of CausalEGM is released on Anaconda.

  • Dec/2022: Preprint paper of CausalEGM is out on arXiv.

  • Aug/2022: Version 0.1.0 of CausalEGM is released on PyPI.

Datasets

Create a CausalEGM/data folder and uncompress the dataset in the CausalEGM/data folder.

Main Reference

If you find CausalEGM useful for your work, please consider citing our PNAS paper:

Qiao Liu, Zhongren Chen, Wing Hung Wong. An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies [J]. PNAS, 2024.

Support

Found a bug or would like to see a feature implemented? Feel free to submit an issue.

Have a question or would like to start a new discussion? You can also always send us an e-mail.

Your help to improve CausalEGM is highly appreciated! For further information visit https://causalegm.readthedocs.io/.

Owner

  • Name: The WH Wong lab at Stanford
  • Login: SUwonglab
  • Kind: organization
  • Email: whwong@stanford.edu
  • Location: Clark Center

GitHub Events

Total
  • Watch event: 8
  • Fork event: 2
Last Year
  • Watch event: 8
  • Fork event: 2

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 184
  • Total Committers: 4
  • Avg Commits per committer: 46.0
  • Development Distribution Score (DDS): 0.065
Top Committers
Name Email Commits
kimmo1019 l****o@b****n 172
Russell-debug 6****g@u****m 9
Qiao Liu l****6@m****n 2
Balasubramanian Narasimhan b****s@g****m 1
Committer Domains (Top 20 + Academic)
mails.tsinghua.edu.cn: 1 buaa.edu.cn: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 3
  • Total pull requests: 2
  • Average time to close issues: 20 days
  • Average time to close pull requests: 1 day
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • mengxiangbin001 (2)
  • githubdemima123456 (1)
Pull Request Authors
  • eltociear (1)
  • bnaras (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 4
  • Total downloads:
    • pypi 103 last-month
    • cran 174 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 21
  • Total maintainers: 2
proxy.golang.org: github.com/suwonglab/causalegm
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.5%
Dependent repos count: 5.7%
Last synced: 6 months ago
proxy.golang.org: github.com/SUwonglab/CausalEGM
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.5%
Dependent repos count: 5.7%
Last synced: 6 months ago
pypi.org: causalegm

CausalEGM: an encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

  • Versions: 16
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 103 Last month
Rankings
Dependent packages count: 6.6%
Downloads: 10.0%
Stargazers count: 11.0%
Forks count: 14.5%
Average: 14.6%
Dependent repos count: 30.6%
Maintainers (1)
kimmo
Last synced: 6 months ago
cran.r-project.org: RcausalEGM

A General Causal Inference Framework by Encoding Generative Modeling

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 174 Last month
Rankings
Stargazers count: 8.9%
Forks count: 11.3%
Dependent packages count: 29.8%
Average: 35.0%
Dependent repos count: 35.5%
Downloads: 89.7%
Maintainers (1)
liuqiao@stanford.edu
Last synced: 6 months ago

Dependencies

r-package/RcausalEGM/DESCRIPTION cran
  • R >= 3.6.3 depends
  • reticulate * imports
docs/requirements.txt pypi
  • nbsphinx *
  • sphinx_autodoc_typehints *
src/CausalEGM.egg-info/requires.txt pypi
  • pandas *
  • python-dateutil *
  • scikit-learn *
  • tensorflow >=2.8.0
src/setup.py pypi
  • pandas *
  • python-dateutil *
  • scikit-learn *
  • tensorflow >=2.8.0