CausalML Diabetes Study Tutorial - UMD Classes -- Tutorials

This folder contains the setup for running CausalML tutorials within a containerized environment

Quick Start¶

From the root of the repository, change your directory to the CausalML tutorial folder:
```
> cd tutorials/CausalML_Diabetes_Study
```
Once the location has been changed to the repo run the command to build the image to run dockers:
```
> ./docker_build.sh
```
Download the CDC BRFSS Diabetes dataset from: https://archive.ics.uci.edu/dataset/891/cdc+diabetes+health+indicators
- Place the downloaded file in data/unprocessed/
- Note: the data/unprocessed/ directory is tracked using .gitkeep but the dataset itself must be downloaded separately
Once the docker has been built you can then go ahead and run the container and launch jupyter notebook using the created image using the command:
```
> ./docker_jupyter.sh
```
Once the ./docker_jupyter.sh script is running, you can execute the tutorials
For more informations on the Docker build system refer to Project template readme

Work through the following notebooks in order:

CausalML.API.ipynb: Tutorial notebook demonstrating the application of CausalML methods for causal effect estimation
- Master the fundamental commands and basic configurations of the CausalML framework
CausalML.example.ipynb: Advanced end-to-end causal analysis example
- Estimates the causal impact of lifestyle interventions (dietary modifications, structured exercise) on diabetes-related health outcomes
- Uses the CDC BRFSS dataset with heterogeneous treatment effect estimation via meta-learners
- Rigorously accounts for confounding variables to ensure credible and unbiased effect estimates
utils.py: Utility functions for data loading, preprocessing, modeling, and evaluation required by the example notebooks