基本信息
浏览量:9
职业迁徙
个人简介
Daniel Malinsky's methodological research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. Current research topics include graphical structure learning (a.k.a. causal discovery or causal model selection), semiparametric inference, time series analysis, and missing data. Application areas of particular interest include environmental determinants of health and health disparities. Dr. Malinsky also studies algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, Dr. Malinsky has interests in the philosophy of science and the foundations of statistics.
研究兴趣
论文共 41 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Kristina L Buschur,Tess D Pottinger,Jens Vogel-Claussen,Charles A Powell,Francois Aguet,Norrina B Allen,Kristin Ardlie,David A Bluemke,Peter Durda, Emilia A Hermann,Eric A Hoffman,João A C Lima,
Annals of the American Thoracic Society (2024)
arxiv(2024)
引用0浏览0引用
0
0
The Milbank quarterlyno. 1 (2023): 122-140
The European respiratory journalno. 6 (2023)
引用0浏览0WOS引用
0
0
Hepatology communicationsno. 10 (2023)
UAIpp.358-368, (2023)
引用0浏览0EI引用
0
0
The Journal of Thoracic and Cardiovascular Surgeryno. 5 (2023): e446-e462
引用2浏览0引用
2
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn