Causality Graphical Models in Python
Project description
# CausalGraphicalModels
## Introduction
`causalgraphicalmodels` is a python module for describing and manipulating [Causal Graphical Models](https://en.wikipedia.org/wiki/Causal_graph) and [Structural Causal Models](https://en.wikipedia.org/wiki/Structural_equation_modeling). Behind the scenes it is a light wrapper around the python graph library [networkx](https://networkx.github.io/), together with some CGM specific tools.
It is currently in a very early stage of development. All feedback is welcome.
## Example
For a quick overview of `CausalGraphicalModel`, see [this example notebook](https://github.com/ijmbarr/causalgraphicalmodels/blob/master/notebooks/cgm-examples.ipynb).
## Install
```
pip install causalgraphicalmodels
```
## Resources
My understanding of Causality comes mainly from the reading of the follow work:
- Causality, Pearl, 2009, 2nd Editing. (An overview available [here](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf))
- A fantastic blog post, [If correlation doesn’t imply causation, then what does?](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/) from Michael Nielsen
- [These lecture notes](http://www.math.ku.dk/~peters/jonas_files/scriptChapter1-4.pdf) from Jonas Peters
- The draft of [Elements of Causal Inference](http://www.math.ku.dk/~peters/jonas_files/bookDRAFT5-online-2017-02-27.pdf)
- http://mlss.tuebingen.mpg.de/2017/speaker_slides/Causality.pdf
## Related Packages
- [Causality](https://github.com/akelleh/causality)
- [CausalInference](https://github.com/laurencium/causalinference)
- [DoWhy](https://github.com/Microsoft/dowhy)
## Introduction
`causalgraphicalmodels` is a python module for describing and manipulating [Causal Graphical Models](https://en.wikipedia.org/wiki/Causal_graph) and [Structural Causal Models](https://en.wikipedia.org/wiki/Structural_equation_modeling). Behind the scenes it is a light wrapper around the python graph library [networkx](https://networkx.github.io/), together with some CGM specific tools.
It is currently in a very early stage of development. All feedback is welcome.
## Example
For a quick overview of `CausalGraphicalModel`, see [this example notebook](https://github.com/ijmbarr/causalgraphicalmodels/blob/master/notebooks/cgm-examples.ipynb).
## Install
```
pip install causalgraphicalmodels
```
## Resources
My understanding of Causality comes mainly from the reading of the follow work:
- Causality, Pearl, 2009, 2nd Editing. (An overview available [here](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf))
- A fantastic blog post, [If correlation doesn’t imply causation, then what does?](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/) from Michael Nielsen
- [These lecture notes](http://www.math.ku.dk/~peters/jonas_files/scriptChapter1-4.pdf) from Jonas Peters
- The draft of [Elements of Causal Inference](http://www.math.ku.dk/~peters/jonas_files/bookDRAFT5-online-2017-02-27.pdf)
- http://mlss.tuebingen.mpg.de/2017/speaker_slides/Causality.pdf
## Related Packages
- [Causality](https://github.com/akelleh/causality)
- [CausalInference](https://github.com/laurencium/causalinference)
- [DoWhy](https://github.com/Microsoft/dowhy)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for causalgraphicalmodels-0.0.3.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 29e80150fbea150de9092e60b1024aabb295f0faf823ffd400d77cad92c59604 |
|
MD5 | 876bba3a67f0cfa47dad7ca540bf4495 |
|
BLAKE2b-256 | 134a736683ab973ba59ebaecec1d6283d7fccc7c79fbdb3a89e5924e7ac8d29a |
Close
Hashes for causalgraphicalmodels-0.0.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3c64fd0e06c3e2e4a66ce11d39c5df0ba12b0616189631aabaa75d4eab140aed |
|
MD5 | 8ec08781c002186f17d581ceac36679c |
|
BLAKE2b-256 | 873452130604f3824c6df708cd2ba286c19d174e46f4a8bf6ee7e22dd18fa0de |