Research map / Probabilistic and scientific modeling / Causal discovery
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select predictive features only when causal inference supports their conditional distribution being invariant across domains | Random Forest feature selection · Synthetic data with large intervention perturbations | L2 loss | Helped |
| Select predictive features only when causal inference supports their conditional distribution being invariant across domains | Random Forest feature selection · IMPC hematology dataset | L2 loss | No clear effect |
| Use invariant-feature selection when the target distribution is very similar to the source distributions | Random Forest feature selection · Synthetic data with small perturbation (γ=0.1) | L2 loss | Hurt |
| Increase intervention-effect scale | Causal invariant-feature selection · Synthetic data | L2 loss | Helped |
| Increase samples per regime | Causal invariant-feature selection · Synthetic data | prediction accuracy | Helped |
| Abstain when no candidate feature set can be established as a separating set | Causal invariant-feature selection · IMPC hematology dataset | prediction coverage: 170 cases out of 1000 | Hurt |
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