Research map / Probabilistic and scientific modeling / Treatment effects
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DRIV instead of DMLATEIV with LM nuisance models | ATE estimation · TripAdvisor 2018 experiment | ATE estimate: 0.117 -> 0.113 | No clear effect |
| Use DRIV instead of DMLATEIV with GB nuisance models | ATE estimation · TripAdvisor 2018 experiment | ATE estimate: 0.127 -> 0.125 | No clear effect |
| Use DRIV instead of DMLATEIV with LM nuisance models | ATE estimation · NLSYM observational data | ATE estimate: 0.137 -> 0.072 | Mixed |
| Use DRIV instead of DMLATEIV with GBM nuisance models | ATE estimation · NLSYM observational data | ATE estimate: 0.157 -> 0.041 | Mixed |
| Use DRIV instead of DMLATEIV with LM nuisance models | ATE estimation · NLSYM semi-synthetic data | 95% CI coverage: 52% -> 98% | Helped |
| Use DRIV instead of DMLATEIV with GBM nuisance models | ATE estimation · NLSYM semi-synthetic data | 95% CI coverage: 72% -> 93% | Helped |
| Use DRIV instead of DMLATEIV | ATE confidence intervals · TripAdvisor semi-synthetic data | 95% CI coverage: 26% -> 94% | Helped |
| Use DRIV instead of DMLATEIV | ATE estimation · NLSYM semi-synthetic data | ATE estimate: DMLATEIV 0.651 and 0.653; DRIV 0.546 and 0.650; true ATE 0.609 | Helped |
Closest papers by meaning
- A Meta-learner for Heterogeneous Effects in Difference-in-Differences (ICML 2025 · similarity 0.89)
- A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding (NeurIPS 2024 · similarity 0.88)
- Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data (NeurIPS 2024 · similarity 0.88)
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- Learning Representations of Instruments for Partial Identification of Treatment Effects (ICML 2025 · similarity 0.87)
- AutoCATE: End-to-End, Automated Treatment Effect Estimation (ICML 2025 · similarity 0.87)
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