Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review-Reference-Cited by-同舟云学术

Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review

Author:

Colnet Bénédicte¹,Mayer Imke²,Chen Guanhua³,Dieng Awa⁴,Li Ruohong⁵,Varoquaux Gaël⁶,Vert Jean-Philippe⁷,Josse Julie⁸,Yang Shu⁹

Affiliation:

1. Bénédicte Colnet is Ph.D. candidate, Soda project-team, INRIA Saclay, Palaiseau, France

2. Imke Mayer is Research Scientist, Owkin, London, UK

3. Guanhua Chen is Associate Professor, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, USA

4. Awa Dieng is Research Associate, Google DeepMind, Montreal, Canada

5. Ruohong Li is Senior Machine Learning Scientist, Microsoft, Kirkland, WA 98033 , USA

6. Gaël Varoquaux is Research Director, Soda project-team, INRIA Saclay, Paries, France

7. Jean-Philippe Vert is Chief R&D Officer, Owkin, Paris, France

8. Julie Josse is Head, Premedical project Inria team, University of Montpellier, France

9. Shu Yang is Associate Professor, Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695, USA

Publisher

Institute of Mathematical Statistics

Reference169 articles.

3. R CORE TEAM (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

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2. Causal Inference with Latent Variables: Recent Advances and Future Prospectives;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Causal inference on human behaviour;Nature Human Behaviour;2024-08-23