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$β$-Intact-VAE: Identifying and Estimating Causal Effects under
  Limited Overlap

βββ-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap

11 October 2021
Pengzhou (Abel) Wu
Kenji Fukumizu
    CML
ArXiv (abs)PDFHTML

Papers citing "$β$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap"

32 / 32 papers shown
Title
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
Ruichu Cai
Junjie Wan
Weilin Chen
Zeqin Yang
Zijian Li
Peng Zhen
Jiecheng Guo
CML
139
1
0
08 May 2025
Towards Principled Causal Effect Estimation by Deep Identifiable Models
Towards Principled Causal Effect Estimation by Deep Identifiable Models
Pengzhou (Abel) Wu
Kenji Fukumizu
BDLOODCML
63
3
0
30 Sep 2021
Deconfounding Scores: Feature Representations for Causal Effect
  Estimation with Weak Overlap
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
Alexander DÁmour
Alexander M. Franks
CML
46
10
0
12 Apr 2021
Estimating Average Treatment Effects with Support Vector Machines
Estimating Average Treatment Effects with Support Vector Machines
Alexander Tarr
Kosuke Imai
57
8
0
23 Feb 2021
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding
Pengzhou (Abel) Wu
Kenji Fukumizu
CML
72
13
0
17 Jan 2021
Increasing the efficiency of randomized trial estimates via linear
  adjustment for a prognostic score
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
Alejandro Schuler
D. Walsh
D.F. Hall
J. Walsh
Charles K. Fisher
66
35
0
17 Dec 2020
Latent Causal Invariant Model
Latent Causal Invariant Model
Xinwei Sun
Botong Wu
Xiangyu Zheng
Chang-Shu Liu
Wei Chen
Tao Qin
Tie-Yan Liu
OODCMLBDL
68
14
0
04 Nov 2020
Estimating Individual Treatment Effects using Non-Parametric Regression
  Models: a Review
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review
A. Caron
G. Baio
I. Manolopoulou
CML
85
55
0
14 Sep 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware
  Models
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
Andrew Jesson
Sören Mindermann
Uri Shalit
Y. Gal
CML
59
74
0
01 Jul 2020
Treatment effect estimation with disentangled latent factors
Treatment effect estimation with disentangled latent factors
Weijia Zhang
Lin Liu
Jiuyong Li
CML
78
89
0
29 Jan 2020
Generalization Bounds and Representation Learning for Estimation of
  Potential Outcomes and Causal Effects
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Fredrik D. Johansson
Uri Shalit
Nathan Kallus
David Sontag
CMLOOD
95
100
0
21 Jan 2020
Disentanglement by Nonlinear ICA with General Incompressible-flow
  Networks (GIN)
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Peter Sorrenson
Carsten Rother
Ullrich Kothe
DRLCML
73
121
0
14 Jan 2020
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble
  Method
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
Pengzhou (Abel) Wu
Kenji Fukumizu
CML
87
27
0
07 Jan 2020
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem
Diederik P. Kingma
Ricardo Pio Monti
Aapo Hyvarinen
OOD
77
598
0
10 Jul 2019
Characterization of Overlap in Observational Studies
Characterization of Overlap in Observational Studies
Michael Oberst
Fredrik D. Johansson
Dennis L. Wei
Tian Gao
G. Brat
David Sontag
Kush R. Varshney
CML
50
22
0
09 Jul 2019
An Introduction to Variational Autoencoders
An Introduction to Variational Autoencoders
Diederik P. Kingma
Max Welling
BDLSSLDRL
124
2,372
0
06 Jun 2019
Adapting Neural Networks for the Estimation of Treatment Effects
Adapting Neural Networks for the Estimation of Treatment Effects
Claudia Shi
David M. Blei
Victor Veitch
CML
152
376
0
05 Jun 2019
Support and Invertibility in Domain-Invariant Representations
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson
David Sontag
Rajesh Ranganath
79
164
0
08 Mar 2019
Using Embeddings to Correct for Unobserved Confounding in Networks
Using Embeddings to Correct for Unobserved Confounding in Networks
Victor Veitch
Yixin Wang
David M. Blei
CML
76
58
0
11 Feb 2019
Overlap in Observational Studies with High-Dimensional Covariates
Overlap in Observational Studies with High-Dimensional Covariates
Alexander DÁmour
Peng Ding
Avi Feller
Lihua Lei
Jasjeet Sekhon
159
194
0
07 Nov 2017
Causal Effect Inference with Deep Latent-Variable Models
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos
Uri Shalit
Joris Mooij
David Sontag
R. Zemel
Max Welling
CMLBDL
225
749
0
24 May 2017
Bayesian Inference of Individualized Treatment Effects using Multi-task
  Gaussian Processes
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed Alaa
M. Schaar
CML
177
304
0
10 Apr 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
682
29,183
0
09 Sep 2016
Tutorial on Variational Autoencoders
Tutorial on Variational Autoencoders
Carl Doersch
BDLDRL
101
1,749
0
19 Jun 2016
Learning Representations for Counterfactual Inference
Learning Representations for Counterfactual Inference
Fredrik D. Johansson
Uri Shalit
David Sontag
CMLOODBDL
306
729
0
12 May 2016
Estimation and Inference of Heterogeneous Treatment Effects using Random
  Forests
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager
Susan Athey
SyDaCML
382
2,496
0
14 Oct 2015
Nonparametric instrumental variable estimation under monotonicity
Nonparametric instrumental variable estimation under monotonicity
Denis Chetverikov
Daniel Wilhelm
66
56
0
19 Jul 2015
Semi-Supervised Learning with Deep Generative Models
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma
Danilo Jimenez Rezende
S. Mohamed
Max Welling
GANSSLBDL
105
2,746
0
20 Jun 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
484
16,922
0
20 Dec 2013
Robust Inference on Average Treatment Effects with Possibly More
  Covariates than Observations
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
M. Farrell
339
343
0
18 Sep 2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation
  Distances
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Marco Cuturi
OT
222
4,294
0
04 Jun 2013
Causal inference using the algorithmic Markov condition
Causal inference using the algorithmic Markov condition
Dominik Janzing
Bernhard Schölkopf
CML
163
307
0
23 Apr 2008
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