Search Results for author: Yameng Liu

Found 3 papers, 2 papers with code

Interpretable Almost-Matching-Exactly With Instrumental Variables

1 code implementation27 Jun 2019 M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i. e., the presence of unobserved covariates linking treatments and outcomes.

Interpretable Almost Matching Exactly for Causal Inference

3 code implementations18 Jun 2018 Yameng Liu, Aw Dieng, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

Causal Inference

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

no code implementations19 Jul 2017 Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets.

Causal Inference

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