Research map / Probabilistic and scientific modeling / Treatment effects
Learning Triggers for Heterogeneous Treatment Effects
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use validation data to penalize effects that fail to generalize | Causal trees for trigger-based HTE · NMES | SMAPE error: 0.982 -> 0.597 | Helped |
| Use validation data to penalize effects that fail to generalize | Causal trees for trigger-based HTE · Cloud Storage | SMAPE error: 0.745 -> 0.607 | Helped |
| Add an honest variance penalty to the validation-based method (ablation) | CT-L · NMES | SMAPE error: 0.597 -> 0.417 | Helped |
| Add an honest variance penalty to the validation-based method (ablation) | CT-L · Cloud Storage | SMAPE error: 0.607 -> 0.613 | No clear effect |
| Use validation data as a hybrid estimation and validation set (ablation) | CT-L · NMES | SMAPE error: 0.597 -> 0.416 | Helped |
| Use validation data as a hybrid estimation and validation set (ablation) | CT-L · Cloud Storage | SMAPE error: 0.607 -> 0.719 | Hurt |
| Prune trees to retain statistically significant leaves (ablation) | Trigger-based causal trees · Cloud Storage | SMAPE error: 0.719 -> 0.396 | Helped |
| Prune trees to retain statistically significant leaves (ablation) | Trigger-based causal trees · NMES | SMAPE error: 0.416 -> 0.416 | No clear effect |
| Consider more possible trigger values (ablation) | CT-HV · NMES | SMAPE error: 0.741 compared to 0.416 | Helped |
| Add validation-based generalization and honest variance penalties | Causal trees · ACIC 2018 | SMAPE error: 0.685 -> 0.189 | Helped |
Closest papers by meaning
- Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments (NeurIPS 2019 · similarity 0.89)
- A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding (NeurIPS 2024 · similarity 0.87)
- Hierarchical and Density-based Causal Clustering (NeurIPS 2024 · similarity 0.87)
- Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data (NeurIPS 2024 · similarity 0.86)
- A Meta-learner for Heterogeneous Effects in Difference-in-Differences (ICML 2025 · similarity 0.86)
- Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments (ICML 2024 · similarity 0.86)
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