Fetching the paper…
Reading the bibliography…
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 features belong to a single treatment group.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
Earlier work this paper cites.
Treatment effect estimation with disentangled latent factors
Weijia Zhang, Lin Liu, and Jiuyong Li · 2001
Earlier work this paper cites.
Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
Earlier work this paper cites.
The prognostic analogue of the propensity score
Ben B Hansen · 2008
Earlier work this paper cites.
Why does birthweight vary among ethnic groups in the uk? findings from the millennium cohort study
Yvonne Kelly, Lidia Panico, Mel Bartley, Michael Marmot, James Nazroo, and Amanda Sacker · 2009
Earlier work this paper cites.
Causality: models, reasoning and inference
Judea Pearl · 2009
Earlier work this paper cites.
Causal inference using the algorithmic markov condition
Dominik Janzing and Bernhard Scholkopf · 2010
Earlier work this paper cites.
Matching Methods for Causal Inference: A Review and a Look Forward
Elizabeth A. Stuart · 2010
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Earlier work this paper cites.
Quantile models with endogeneity
Victor Chernozhukov and Christian Hansen · 2013
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Efficient discovery of overlapping communities in massive networks
Prem K Gopalan and David M Blei · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Identification and identification failure for treatment effects using structural systems
Halbert White and Karim Chalak · 2013
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
Snap datasets: Stanford large network dataset collection, 2014
Jure Leskovec and Andrej Krevl · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Estimating conditional average treatment effects
Jason Abrevaya, Yu-Chin Hsu, and Robert P Lieli · 2015
Earlier work this paper cites.
Robust inference on average treatment effects with possibly more covariates than observations
Max H Farrell · 2015
Earlier work this paper cites.
Identification and shape restrictions in nonparametric instrumental variables estimation
Joachim Freyberger and Joel L Horowitz · 2015
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Earlier work this paper cites.
Tutorial on variational autoencoders
Carl Doersch · 2016
Earlier work this paper cites.
Efficiency of thin and thick markets
Li Gan and Qi Li · 2016
Earlier work this paper cites.
Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
Earlier work this paper cites.
Bayesian inference of individualized treatment effects using multi-task gaussian processes
Ahmed M Alaa and Mihaela van der Schaar · 2017
Cited alongside, same era.
Nonparametric instrumental variable estimation under monotonicity
Denis Chetverikov and Daniel Wilhelm · 2017
Cited alongside, same era.
Estimation of conditional and marginal odds ratios using the prognostic score
David Hajage, Yann De Rycke, Guillaume Chauvet, and Florence Tubach · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Joint sufficient dimension reduction and estimation of conditional and average treatment effects
Ming-Yueh Huang and Kwun Chuen Gary Chan · 2017
Cited alongside, same era.
Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
Peter Sorrenson, Carsten Rother, and Ullrich Köthe · 2019
Later among the works it cites.
Jennifer E Starling, Catherine E Aiken, Jared S Murray, Annettee Nakimuli, and James G Scott · 2019
Later among the works it cites.
Using embeddings to correct for unobserved confounding in networks
Victor Veitch, Yixin Wang, and David Blei · 2019
Later among the works it cites.
Estimating individual treatment effects using non-parametric regression models: a review
Alberto Caron, Ioanna Manolopoulou, and Gianluca Baio · 2020
Later among the works it cites.
Quantifying common support between multiple treatment groups using a contrastive-vae
Wangzhi Dai and Collin M Stultz · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Nonparametric knn estimation with monotone constraints
Zheng Li, Guannan Liu, and Qi Li · 2017
Cited alongside, same era.
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
Cited alongside, same era.
On estimating regression-based causal effects using sufficient dimension reduction
Wei Luo, Yeying Zhu, and Debashis Ghosh · 2017
Cited alongside, same era.
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
Cited alongside, same era.
The econometrics of shape restrictions
Denis Chetverikov, Andres Santos, and Azeem M Shaikh · 2018
Cited alongside, same era.
Local average and quantile treatment effects under endogeneity: a review
Martin Huber and Kaspar Wüthrich · 2018
Cited alongside, same era.
Later among the works it cites.
Overlap in observational studies with high-dimensional covariates
Alexander D’Amour, Peng Ding, Avi Feller, Lihua Lei, and Jasjeet Sekhon · 2020
Later among the works it cites.
Causal Inference: What If
Miguel A. Hernan and James M. Robins · 2020
Later among the works it cites.
Inference on finite-population treatment effects under limited overlap
Han Hong, Michael P Leung, and Jessie Li · 2020
Later among the works it cites.
Identifying causal-effect inference failure with uncertainty-aware models
Andrew Jesson, Sören Mindermann, Uri Shalit, and Yarin Gal · 2020
Later among the works it cites.
Fredrik D Johansson, Uri Shalit, Nathan Kallus, and David Sontag · 2020
Later among the works it cites.
Reconsidering generative objectives for counterfactual reasoning
Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, and Lawrence Carin · 2020
Later among the works it cites.
Characterization of overlap in observational studies
Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, and Kush Varshney · 2020
Later among the works it cites.
Modern algorithms for matching in observational studies
Paul R Rosenbaum · 2020
Later among the works it cites.
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, and Charles Fisher · 2020
Later among the works it cites.
Xinwei Sun, Botong Wu, Chang Liu, Xiangyu Zheng, Wei Chen, Tao Qin, and Tie-yan Liu · 2020
Later among the works it cites.
An introduction to proximal causal learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui, Xu Shi, and Wang Miao · 2020
Later among the works it cites.
Targeted vae: Structured inference and targeted learning for causal parameter estimation
Matthew James Vowels, Necati Cihan Camgoz, and Richard Bowden · 2020
Later among the works it cites.
Changing trends of birth weight with maternal age: a cross-sectional study in xi’an city of northwestern china
Shanshan Wang, Liren Yang, Li Shang, Wenfang Yang, Cuifang Qi, Liyan Huang, Guilan Xie, Ruiqi Wang, and Mei Chun Chung · 2020
Later among the works it cites.
Finite-sample optimal estimation and inference on average treatment effects under unconfoundedness
Timothy B Armstrong and Michal Kolesár · 2021
Closest in time.
Stéphane Bonhomme and Martin Weidner · 2021
Closest in time.
Deconfounding scores: Feature representations for causal effect estimation with weak overlap
Alexander D’Amour and Alexander Franks · 2021
Closest in time.
A critical look at the identifiability of causal effects with deep latent variable models
Severi Rissanen and Pekka Marttinen · 2021
Closest in time.
Estimating average treatment effects with support vector machines
Alexander Tarr and Kosuke Imai · 2021
Closest in time.
Identifying treatment effects under unobserved confounding by causal representation learning
Pengzhou Abel Wu and Kenji Fukumizu · 2021
Closest in time.