Perform a causal inference task using Pearl’s framework;
Infer the causal graph from observational data and then validate the graph;
Merge machine learning with causal inference;
Explore more about the challenge
dataset on Kaggle
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Table of Contents
A common frustration in the industry, especially when it comes to getting business insights from tabular data, is that the most interesting questions (from their perspective) are often not answerable with observational data alone. These questions can be similar to: “What will happen if I halve the price of my product?” “Which clients will pay their debts only if I call them?” Judea Pearl and his research group have developed in the last decades a solid theoretical framework to deal with that, but the first steps toward merging it with mainstream machine learning are just beginning.
The causal graph is a central object in the framework mentioned above, but it is often unknown, subject to personal knowledge and bias, or loosely connected to the available data.
Here's What this module can do:
- Read the data
- Perform exploratory analysis on it
- Extract features and scale the extracted feature
- Split the data into training and hold-out set
- Create casual graph using different technique
- Examine the model performance based on the graph
A list of commonly used resources that we find helpful are listed in the acknowledgements.
Resoures that used in this project are :
You can get a local copy up and running follow these simple example steps.
- Clone the repo
git clone https://github.com/Casualty-Challenge/Breast_Cancer_Causality_Inference.git
- Install the setup.py
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Biniam Sisay - binasisayet8790@gmail.com
Euel Fantaye - euelfantaye@gmail.com
Yosef Engdawork - josephnewgold1@gmail.com
Maelaf Tegegn - maelaf17@gmail.com
Project Link: https://github.com/Casualty-Challenge/Breast_Cancer_Causality_Inference.git
https://github.com/Casualty-Challenge/Breast_Cancer_Causality_Inference.git