AutoCATE: End-to-End, Automated Treatment Effect Estimation

— AutoML for causal effect estimation —

AutoCATE is an open-source Python package for automated, end-to-end estimation of Conditional Average Treatment Effects (CATE). Designed to simplify the complexities of causal inference, AutoCATE enables practitioners and researchers to quickly build robust ML pipelines for causal effect/heterogeneous treatment effect estimation in only four lines of code.

✨ Key Features

✔️ Automated ML Pipelines: Automatically builds pipelines and handles preprocessing, ML algorithm selection, hyperparameter optimization, and ensembling.

✔️ Custom Evaluation Protocols: Incorporates different risk measures (e.g., DR-risk, T-risk) and risk metrics (e.g., AUQC) tailored for causal inference.

✔️ Low-Code API: Effortlessly predict treatment effects with minimal setup.

📦 Installation

Install directly from PyPI:

pip install autocate

Requires Python ≥ 3.8.

🛠 Quick start

from autocate import AutoCATE

# Initialize AutoCATE
autocate = AutoCATE()

# Fit model on training data
autocate.fit(X_train, t_train, yf_train)

# Predict CATE for new data
cate_pred = autocate.predict(X_test)

⚙️ How It Works

AutoCATE operates in three stages:

1️⃣ Evaluation: Optimize the ML pipelines underlying the risk measure(s) (e.g., R-risk) for robust pipeline selection.

2️⃣ Estimation: Optimizing ML pipelines for CATE estimation, combining preprocessors, metalearners, and baselearners.

3️⃣ Ensembling: Combines the top-performing pipelines to create a final model.

❓ Why Choose AutoCATE?

Estimating causal effects requires dealing with unique challenges 🚨

❌ Evaluation is a challenge due to lack of ground truth CATE and covariate shift caused by confounding variables.

❌ Causal metalearners combine different ML algorithms and are complex to tune.

❌ No clear, established practices for preprocessing and ensembling.

AutoCATE eliminates these barriers by automating the entire process, making state-of-the-art CATE estimation accessible for everyone.

📄 License

This project is licensed under the MIT License.