Research map / Probabilistic and scientific modeling / Treatment effects
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| separate outcome-specific regression structure (non-stationary kernel / separate surfaces) instead of single-surface model | CATE estimators on IHDP · IHDP | out-of-sample √PEHE | Helped |
| adding doubly-robust hyperparameter tuning and non-stationary kernel to a stationary GP | GP model for CATE · IHDP | out-of-sample √PEHE: roughly 2-fold PEHE improvement | Helped |
| over-smoothing the NSGP prior (beta0 > alpha0) (ablation) | NSGP on Brownian synthetic data · synthetic Brownian response surfaces | log-log PEHE learning rate | Hurt |
| increasing selection bias strength eta from 0 to 1/2 (ablation) | NSGP on Brownian synthetic data · synthetic Brownian response surfaces | log-log PEHE slope | No clear effect |
| using a stationary kernel instead of non-stationary kernel (ablation) | GP model on Brownian synthetic data · synthetic Brownian response surfaces | learning rate | Hurt |
| removing selection-bias correction (non-stationary kernel without doubly-robust tuning) (ablation) | NSGP on Brownian synthetic data · synthetic Brownian response surfaces | PEHE offset | Hurt |
Closest papers by meaning
- Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model (AAAI 2024 · similarity 0.90)
- A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding (NeurIPS 2024 · similarity 0.89)
- Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark (ICLR 2025 · similarity 0.89)
- Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data (NeurIPS 2024 · similarity 0.88)
- Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction (ICML 2024 · similarity 0.88)
- Learning Representations of Instruments for Partial Identification of Treatment Effects (ICML 2025 · similarity 0.88)
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