Talks and presentations

Improving the Reliability of Real-World Evidence Generation from Large-Scale Observational Data

April 06, 2023

Conference proceedings talk, University of Colorado Department of Biomedical Informatics, Virtual

This talk highlights the work I did during my PhD on developing causal machine learning methods to improve the reliability of real-world evidence generation from electronic health records.

Adjusting for indirectly measured confounding using LSPS

December 06, 2022

Talk, OHDSI Community Call, Virtual

This talk went over the causal inference method called large-scale propensity score (LSPS) and its potential in adjusting for unmeasured confounding in comparative studies using observational healthcare data (e.g., electronic health records and insurance claims).

When does statistical equality meet health equity?

October 14, 2022

Talk, OHDSI Global Symposium 2022, Bethesda, Maryland, USA

This 7-min talk showcase our research on quantifying fairness of treatment allocation from a causal perspective.

Linying Zhang

Talks and presentations

Improving the Reliability of Real-World Evidence Generation from Large-Scale Observational Data

Adjusting for indirectly measured confounding using LSPS

When does statistical equality meet health equity?