bytedance / CausalMatch
CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about automation in decision-making process.
Why this job: works in the areas we cover, but does not say which job it does
What it does
This package was designed and built as part of the ByteDance data science research program with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. Our toolkit possess the following features:
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 8 months ago
- Stars
- 113
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.0.5
- Released 20 months ago
- Built with
- Jupyter Notebook
- 72% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 0% closed
- 1 still open
- Pull requests
- 1 merged
- 0 waiting
- Releases
- 6
- 1 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Jupyter Notebook 72%
- Python 28%
Topics
- causal-inference
- econometrics
- machine-learning
Project record
- JobOther AI tools
- StatusListed
- Commits61
- Forks12
- Watching7
- Stars in 28 days0
- CreatedAug 1, 2024
- First seen by usNov 7, 2026
- CheckedNov 8, 2026