JungHo Lee
@jungholee.bsky.social
PhD student in Statistics & Data Science at CMU
Causal inference, machine learning, nonparametric statistics with applications to social science
jungholeestat.github.io
Causal inference, machine learning, nonparametric statistics with applications to social science
jungholeestat.github.io
Reposted by JungHo Lee
Wenxuan Guo, Tengyuan Liang, Panos Toulis
Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
https://arxiv.org/abs/2502.16042
Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
https://arxiv.org/abs/2502.16042
February 25, 2025 at 8:16 AM
Wenxuan Guo, Tengyuan Liang, Panos Toulis
Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
https://arxiv.org/abs/2502.16042
Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
https://arxiv.org/abs/2502.16042
Reposted by JungHo Lee
My work on network regression and mediation in latent space models is now published at JMLR!
jmlr.org/papers/v26/2...
jmlr.org/papers/v26/2...
February 10, 2025 at 11:30 PM
My work on network regression and mediation in latent space models is now published at JMLR!
jmlr.org/papers/v26/2...
jmlr.org/papers/v26/2...
Reposted by JungHo Lee
Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!
Lars van der Laan, Aurelien Bibaut, Nathan Kallus, Alex Luedtke
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
January 22, 2025 at 1:54 PM
Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!
Reposted by JungHo Lee
PLEASE RT :)
Submit to the annual Network Science and Economics Conference, the largest US event for network theory and empirical research in and adjacent to economics
Deadline Jan 30 (soon!)
Conference Apr 11-13 at Stanford
Submit to the annual Network Science and Economics Conference, the largest US event for network theory and empirical research in and adjacent to economics
Deadline Jan 30 (soon!)
Conference Apr 11-13 at Stanford
January 17, 2025 at 10:40 PM
PLEASE RT :)
Submit to the annual Network Science and Economics Conference, the largest US event for network theory and empirical research in and adjacent to economics
Deadline Jan 30 (soon!)
Conference Apr 11-13 at Stanford
Submit to the annual Network Science and Economics Conference, the largest US event for network theory and empirical research in and adjacent to economics
Deadline Jan 30 (soon!)
Conference Apr 11-13 at Stanford
Excited to share a new paper on ML-assisted randomization tests for detecting various treatment effects!
Wenxuan Guo, JungHo Lee, Panos Toulis
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments
https://arxiv.org/abs/2501.07722
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments
https://arxiv.org/abs/2501.07722
January 17, 2025 at 3:43 AM
Excited to share a new paper on ML-assisted randomization tests for detecting various treatment effects!