Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.
no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Counterfactual prediction is a fundamental task in decision-making.
Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability.