High-dimensional Heterogeneous Effects
High-Dimensional Heterogeneous Effects is a statistical framework in causal inference that characterizes the variation in treatment effects across a large number of covariates or subgroups, where the dimensionality of effect modifiers approaches or exceeds the sample size, necessitating regularization or dimensionality reduction techniques to identify structured heterogeneity.
27
Publications
1.1K
Citations
81
Authors
56
Institutions
Publications per year
2017–2026
18
27
Some methods for heterogeneous treatment effect estimation in high dimensions.
Scott Powers, Junyang Qian, Kenneth Jung et al. · PubMed · 2018 · 192 citations
Causal Inference Meets Machine Learning
Peng Cui, Zheyan Shen, Sheng Li et al. · 2020 · 49 citations
Program evaluation with high-dimensional data
Alexandre Belloni, Victor Chernozhukov, Iván Fernández‐Val et al. · 2017 · 44 citations
1–5 of 27
81
Massachusetts Institute of Technology, United States
4 publications · 227 citations
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3
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2
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2
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2
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1–5 of 81
56
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
3 h-index
10 publications
202 citations
Politecnico di Milano
Milan, Italy
2 h-index
5 publications
195 citations
Nestlé (Switzerland)
Vevey, Switzerland
2 h-index
6 publications
117 citations
Stanford University
Stanford, United States
2 h-index
14 publications
1.5K citations
University of Michigan
Ann Arbor, United States
2 h-index
4 publications
78 citations
Ross School
East Hampton, United States
2 h-index
4 publications
78 citations
The University of Texas at Dallas
Richardson, United States
2 h-index
2 publications
39 citations
Flatiron Health (United States)
New York, United States
1 h-index
1 publications
23 citations
1–8 of 56
Repository · 5 publications · 110 citations
5
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Repository · 3 publications · 62 citations
3
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1 publications · 29 citations
1
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Repository · 1 publications · 20 citations
1
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1 publications · 23 citations
1
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1–5