Research map / Learning theory and algorithms / Kernels and regression theory
Dual Instrumental Variable Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use nonlinear DualIV instead of 2SLS | IV regression · Demand design, n=50 and n=1000 across ρ=0.1–0.9 | Log10 MSE | Helped |
| Use DualIV instead of KernelIV | IV regression · Demand design, n=50 | Log10 MSE | Mixed |
| Use DualIV instead of KernelIV | IV regression · Demand design, n=1000 | Log10 MSE | Mixed |
| Use DeepGMM instead of DualIV | IV regression · Demand design, n=50 | Log10 MSE | Mixed |
| Reduce DeepGMM neural-network initialization variance to 0.1 | DeepGMM · Demand design, n=50 and n=1000 | Log10 MSE variability: 0.1 | Helped |
| Replace explicit first-stage regression with the DualIV saddle-point formulation | Nonlinear IV regression · Demand design, n=50 and n=1000 | Log10 MSE | No clear effect |
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