A Python package for causal inference in quasi-experimental settings
⚡ Executive Summary & Quick Facts
Authoritative SourceCausalPy is CausalPy is an open-source project written in Python: A Python package for causal inference in quasi-experimental settings..
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of October 2026), pymc-labs/CausalPy has accumulated 1,208 GitHub stars and 122 forks. It is written in Python, licensed under Apache-2.0, and ranks #6,554 globally. It is actively maintained with the latest commit recorded on Oct 27, 2026.”
| Repository | pymc-labs/CausalPy ↗ |
|---|---|
| Primary Category | causal inference |
| Language & Stack | Python |
| Software License | Apache-2.0 |
| Community Traction | ★ 1.2K stars · ⑂ 122 forks · #6,554 globally |
| Maintenance | ● Active (Last commit: Oct 27, 2026) · 162 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
🛡️ Repository Maintenance & Health Audit (2026)
Health Score: 97/100 · Grade A+Yes, CausalPy is actively maintained. Continuous telemetry recorded the last code commit on Oct 27, 2026 (2 days ago). Supported by 1,208 stars and 122 community forks, this repository holds a calculated health index of 97/100, reflecting very active (commits within last 14 days) and healthy issue cadence.
Star History
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+30
STARS · LAST 30 DAYS
1
PER DAY
#931
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +30 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
CausalPy gained 30 stars in the last 30 days, about 1 a day, and now has 1.2K. It is about 4 years old and has averaged roughly 302 stars a year. It ranks #931 among Python repositories and #6,554 across all languages on GitHubRepo.
Trending Record
3
DAYS ON TRENDING
#9010
BEST RANK
Oct 27, 2026
FIRST APPEARANCE
Active
STATUS TODAY
CausalPy has maintained a continuous presence across global trending indexes, peaking at #9010. Below is the 30-day activity profile:
💡 Overview
CausalPy is an open-source project written in Python: A Python package for causal inference in quasi-experimental settings.
Engineered for speed, consistency, and developer ease, it solves common hurdles in causal-inference, pymc, quasi-experimental. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install CausalPy⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
CausalPy coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed CausalPy into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.
🚀 Getting Started
Install CausalPy using your package manager: `pip install CausalPy`
Initialize your project workspace or configuration file for CausalPy.
Import CausalPy into your codebase or invoke it directly from your terminal.
Execute your test suite or run `CausalPy --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
pymc-labs/CausalPy is currently ranked #6,554 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #6,549 | newrelic/newrelic-ruby-agent | Ruby | ★ 1.2K | Compare ↗ |
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| #6,549 | jieyefriic/rp-engine | Rust | ★ 1.2K | Compare ↗ |
| #6,554 | pymc-labs/CausalPy This Project | Python | ★ 1.2K | |
| #6,554 | thomas-mauran/chess-tui | Rust | ★ 1.2K | Compare ↗ |
| #6,556 | ethereum/execution-specs | Python | ★ 1.2K | Compare ↗ |
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Frequently Asked Questions
What does CausalPy do? +
A Python package for causal inference in quasi-experimental settings
Is CausalPy actively maintained in 2026? +
Yes, CausalPy is actively maintained. The latest code push was recorded on Oct 27, 2026 (2 days ago). It holds a calculated Maintenance Health Score of 97/100 (A+ rating) with 1,208 stars and 162 open issues.
What language is CausalPy written in? +
The primary language is Python. Topics include: causal-inference, pymc, quasi-experimental, quasi-experiments.
How many stars does CausalPy have? +
CausalPy has 1,208 stars and 122 forks on GitHub.
How does CausalPy rank among GitHub repositories? +
With 1,208 stars, pymc-labs/CausalPy is ranked #6,554 globally across all repositories tracked on GitHubRepo and #931 among Python projects.
What license is CausalPy distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
