autoite 1.0.0

Automated Individual Treatment Effect Estimation via residual-based latent environment discovery

AutoITE: Automated Individual Treatment Effect Estimation

A residual-based approach to causal inference that detects latent heterogeneity through baseline coupling, enabling Just-in-Time discovery of treatment effects.

Key Insight

Traditional causal inference methods condition on observed features, but latent confounders create hidden subgroups with dramatically different treatment responses. AutoITE exploits baseline coupling---the fact that latent confounders affect not just treatment response but also baseline outcomes---to discover these hidden subgroups through residual analysis.

Installation

pip install -r requirements.txt

Quick Start

from autoite import AutoITEEstimator, BimodalityDiagnostic

# Fit the model
model = AutoITEEstimator(k=1000, alpha=1.0)
model.fit(X_train, T_train, Y_train, Y_pre_train)

# Predict individual treatment effects
tau_pred = model.predict(X_test, Y_pre_test)

# Check for hidden subgroups
diag = BimodalityDiagnostic()
diag.fit(X_train, Y_pre_train)
result = diag.quantify_unknown(X_test, Y_pre_test)
print(f"Bimodality Score: {result['bimodality_score']:.4f}")
print(f"Interpretation: {result['interpretation']}")

Architecture

1. Global Ridge: Baseline model predicting pre-treatment outcomes from features
2. Residual Computation: Leave-one-out residuals encode latent causal state
3. Residual Matching: k-NN in residual space finds individuals with similar latent states
4. Local Ridge: Treatment effects estimated from residual neighbors
5. Triage: High-uncertainty cases flagged for expert review

Key Results

From the accompanying paper:

Method	Corr(τ̂, U)	Detection Rate	MAE	Median
Causal Forest	0.00	27.3%	0.230	0.042
X-Learner	0.00	27.1%	0.245	0.045
AutoITE	-0.94	97.5%	0.095	0.034

AutoITE achieves 59% lower MAE than Causal Forest (0.095 vs 0.230). With 15% triage, MAE reduces to 0.042—only 18% of Causal Forest's error—and deaths drop from 8 to 5.

Components

AutoITEEstimator

Core estimator for individual treatment effect prediction.

• k: Number of residual neighbors (default: 1000, or use fraction like 0.10)
• alpha: Ridge regularization strength
• triage_percentile: Fraction of high-uncertainty cases to flag

BimodalityDiagnostic

Detects hidden subgroups via GMM-based residual analysis.

• Bimodality score < 0.01: No hidden structure
• Bimodality score 0.01-0.05: Weak structure
• Bimodality score 0.05-0.10: Moderate structure
• Bimodality score > 0.10: Strong hidden structure (likely latent confounder)

UnexplainedHeterogeneityIndex

Measures whether local models improve over global, indicating heterogeneity not captured by observed features.

Reproducing Paper Results

cd experiments/paper_experiments
python run_all_experiments.py

Real-World Validation

The UCI Student Performance experiment demonstrates AutoITE on real educational data:

cd experiments/paper_experiments
python uci_student_intervention.py

Fundamental Limits

AutoITE can detect latent confounders that affect baseline outcomes (baseline coupling). However, interaction-only confounders---those that affect ONLY treatment response without leaving baseline fingerprints---are fundamentally undetectable by any observational method.

Paper

See paper/auto_ite_final.pdf for the full manuscript:

AutoITE: Residual-Based Individual Treatment Effect Estimation via Baseline Coupling

Jake Peace, November 2025

Data Attribution

UCI Student Performance Dataset

The real-world validation uses the Student Performance dataset from the UCI Machine Learning Repository, provided under the CC BY 4.0 license.

• Creator: Paulo Cortez
• Source: https://archive.ics.uci.edu/dataset/320/student+performance
• DOI: 10.24432/C5TG7T
• Citation: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

License

MIT License - see LICENSE file for details.

Author

Jake Peace (2025)