Search Results for author: Gino Tesei

Found 3 papers, 1 papers with code

Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation

1 code implementation Journal of Biomedical Informatics 2023 Gino Tesei, Stefanos Giampanis, Jingpu Shi, Beau Norgeot

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.

Causal Inference Representation Learning

Mimicking Randomized Controlled Trials to Learn End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation

no code implementations29 Sep 2021 Gino Tesei, Stefanos Giampanis, Beau Norgeot

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.

Representation Learning

Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments

no code implementations29 Sep 2021 Jingpu Shi, Dong Wang, Gino Tesei, Beau Norgeot

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.

Causal Inference

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