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Causal Inference with Conditional Instruments Using Deep Generative Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn CIV and conditioning-set representations from measured pretreatment variables with CIV.VAE instead of relying on supplied instruments or conditioning sets | CIV.VAE · Synthetic datasets, 4k–20k samples | ACE absolute error | Helped |
| Learn CIV and conditioning-set representations from measured pretreatment variables with CIV.VAE instead of relying on supplied instruments or conditioning sets | CIV.VAE · Synthetic 2k | ACE absolute error | Hurt |
| Increase the synthetic sample size | CIV.VAE · Synthetic datasets, 2k–20k samples | ACE absolute error: 1.94$\pm$1.45 -> 0.22$\pm$0.17 | Helped |
| Use CIV.VAE rather than the strongest reported baseline for each synthetic sample size | CIV.VAE · Synthetic 4k–20k | ACE absolute error: 0.95$\pm$0.42 vs 1.10$\pm$0.61; 0.42$\pm$0.34 vs 0.64$\pm$0.50; 0.33$\pm$0.20 vs 0.62$\pm$0.37; 0.26$\pm$0.19 vs 0.63$\p | Helped |
| Use CIV.VAE rather than IV.Tetrad on Sachs | CIV.VAE · Sachs | estimated ACE: 1.5133 vs 1.4301 | Mixed |
| Use CIV.VAE rather than IV.Tetrad on Schoolingreturns | CIV.VAE · Schoolingreturns | estimated ACE: 0.1034 vs 0.0643 | No clear effect |
| Use CIV.VAE rather than FIVR on 401(k) | CIV.VAE · 401(k) | estimated ACE: 0.0752 vs 0.0746 | No clear effect |
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