Causallib

Abstract

1. Introduction

Causal inference analysis enables estimating the causal effect of an intervention on some outcome from real-world non-experimental observational data.

This package provides a suite of causal methods, under a unified scikit-learn-inspired API. It implements meta-algorithms that allow plugging in arbitrarily complex machine learning models. This modular approach supports highly-flexible causal modelling. The fit-and-predict-like API makes it possible to train on one set of examples and estimate an effect on the other (out-of-bag), which allows for a more "honest"1 effect estimation.

The package also includes an evaluation suite. Since most causal-models utilize machine learning models internally, we can diagnose poor-performing models by re-interpreting known ML evaluations from a causal perspective.

2. Objective

A Python package for modular causal inference analysis and model evaluations

This project demonstrates how Python, Machine Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/BiomedSciAI/causallib.git
cd causallib

pip install causallib

from sklearn.linear_model import LogisticRegression
from causallib.estimation import IPW
from causallib.datasets import load_nhefs

data = load_nhefs()
ipw = IPW(LogisticRegression())
ipw.fit(data.X, data.a)
potential_outcomes = ipw.estimate_population_outcome(data.X, data.a, data.y)
effect = ipw.estimate_effect(potential_outcomes[1], potential_outcomes[0])

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by BiomedSciAI and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.