Research map / Probabilistic and scientific modeling / Treatment effects
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use risk-based empirical Bayes CMGP instead of separate GP regression with marginal-likelihood hyperparameter selection | Gaussian process treatment-effect estimation · IHDP in-sample and out-of-sample | √PEHE: 2.1 ± 0.11 -> 0.9 ± 0.07; 2.3 ± 0.14 -> 1.0 ± 0.08 | Helped |
| Use risk-based empirical Bayes CMGP instead of separate GP regression with marginal-likelihood hyperparameter selection | Gaussian process treatment-effect estimation · UNOS in-sample and out-of-sample | √PEHE: 4.1 ± 0.15 -> 1.7 ± 0.10; 4.5 ± 0.20 -> 1.8 ± 0.13 | Helped |
| Use CMGP instead of CFRW | Individual treatment-effect estimation · IHDP in-sample and out-of-sample | √PEHE: 1.0 ± 0.07 -> 0.9 ± 0.07; 1.2 ± 0.08 -> 1.0 ± 0.08 | Mixed |
| Use CMGP instead of CFRW | Individual treatment-effect estimation · UNOS in-sample and out-of-sample | √PEHE: 2.7 ± 0.07 -> 1.7 ± 0.10; 2.9 ± 0.11 -> 1.8 ± 0.13 | Helped |
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