Research map / Probabilistic and scientific modeling / Treatment effects
Causal Estimation with Functional Confounders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use surrogate interventions to adjust for functional confounding | OURS · Simulated causal models A and B | conditional effect RMSE | Helped |
| Increase confounder strength γ (ablation) | OURS · Simulated causal models A and B | conditional effect RMSE | Mixed |
| Reduce treatment variance σ² (ablation) | OURS · Simulated causal models A and B | conditional effect RMSE | Hurt |
| Increase confounder mismatch δ in the estimated surrogate (ablation) | OURS · Simulated causal models A and B | conditional effect RMSE | Hurt |
| Increase outcome sensitivity α to the confounder (ablation) | OURS · Simulated causal models A and B | conditional effect RMSE | Hurt |
| Increase Euler integration step size ℓ (ablation) | OURS · Simulated causal model B | conditional effect RMSE | Hurt |
| Use Euler integration for a linear confounder function (ablation) | OURS · Linear confounder functions | surrogate integration error | No clear effect |
| Apply confounding adjustment rather than relying solely on Lasso coefficients | OURS · Celiac disease GWAS | recovery of previously reported relevant SNPs | Helped |
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