Jun Li

Causal Inference & Discovery · with Chakraborty · 2020

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Causal Inference & Discovery — 9 citations into it
• Statistics and Probability — 8 citations into it

Whom Jun Li trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. James M. Robins
2. Miguel A. Hern'an
3. Yarin Gal
4. Stijn Vansteelandt
5. Peter F. Schulam
6. Liliana Orellana

Papers from Jun Li's perspective

Ordered by how strongly the researchers Jun Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.
2. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
3. Correcting for non-compliance in randomized trials using structural nested mean models
4. Marginal Structural Models and Causal Inference in Epidemiology
5. Reliable Decision Support using Counterfactual Models
6. Variational Learning of Inducing Variables in Sparse Gaussian Processes
7. Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content
8. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
9. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
10. Estimation of the causal effects of time-varying exposures

Their papers

• G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020) — 22 citations