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Found 5 papers, 1 papers with code
Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation
The primary difference between causal effect studies utilizing observational data and RCTs is that for observational data, the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism.
Ranked #1 on
Causal Inference
on Jobs
Mimicking Randomized Controlled Trials to Learn End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation
Additionally, we show that error improvements between our approach and previously published state-of-art methods widen as a function of sample dissimilarity between treated and untreated covariate distributions.
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments
Validation of these models, however, has been a challenge because the ground truth is unknown: only one treatment-outcome pair for each person can be observed.
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision
Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT.
Ranked #6 on
Causal Inference
on IHDP
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacing between visits for each patient, with unequal numbers of data points collected across patients.
