6 citations · 10 across the 5 of their papers we have counts for
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stat.ML2021★ 1 cited
Towards Principled Causal Effect Estimation by Deep Identifiable Models
Pengzhou Wu, Kenji Fukumizu
As an important problem in causal inference, we discuss the estimation of treatment effects (TEs). Representing the confounder as a latent variable, we propose Intact-VAE, a new va…
stat.ML2021
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
Pengzhou Wu, Kenji Fukumizu
As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain feat…
stat.ML2021★ 6 cited
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding
Pengzhou Wu, Kenji Fukumizu
NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 con…