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Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

AAAI 2023 · Time series › Causal and generative time series · approach: Irregular series and imputation · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use LipCDE instead of the strongest listed baseline, OriCDETreatment effect estimation · MIMIC-III blood pressureRMSE%: 10.55 -> 9.19Helped
Use LipCDE instead of CRNTreatment effect estimation · MIMIC-III oxygen saturationRMSE%: 4.17 -> 4.15Helped
Use LipCDE instead of TSDTreatment effect estimation · COVID-19RMSE%: 11.08 -> 7.56Helped
Add hidden confounders to LipCDE (ablation)LipCDE · MIMIC-III blood pressureRMSE%: 10.54 -> 9.19Helped
Add hidden confounders to LipCDE (ablation)LipCDE · MIMIC-III oxygen saturationRMSE%: 4.27 -> 4.15Helped
Add hidden confounders to LipCDE (ablation)LipCDE · COVID-19RMSE%: 10.69 -> 7.56Helped
Apply Lipschitz constraint (ablation)LipCDE · MIMIC-III blood pressureRMSE%: 9.51 -> 9.19Helped
Apply Lipschitz constraint (ablation)LipCDE · MIMIC-III oxygen saturationRMSE%: 4.24 -> 4.15Helped
Apply Lipschitz constraint (ablation)LipCDE · COVID-19RMSE%: 10.5 -> 7.56Helped
Include high-frequency components (ablation)LipCDE · MIMIC-III blood pressureRMSE%: 10.10 -> 9.19Helped
Include high-frequency components (ablation)LipCDE · MIMIC-III oxygen saturationRMSE%: 4.21 -> 4.15Helped
Include high-frequency components (ablation)LipCDE · COVID-19RMSE%: 10.69 -> 7.56Helped

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