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This paper addresses the use of neural networks for the estimation of treatment effects from observational data.
“Using Text Embeddings for Causal Inference”
Victor Veitch, Dhanya Sridhar and David. Blei · 1905
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“The central role of the propensity score in observational studies for causal effects”
Paul. Rosenbaum and Donal. Rubin · 1983
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“The effect of weight trimming on nonlinear survey estimates”
Frank Potter · 1993
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“Infant mortality statistics from the linked birth/infant death”
Marian MacDorman and Jonnae Atkinson · 1998
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“Adjusting for nonignorable drop-out using semiparametric nonresponse models”
Daniel Scharfstein, Andrea Rotnitzky and James Robins · 1999
Earlier work this paper cites.
“Robust Estimation in Sequentially Ignorable Missing Data and Causal Inference Models”
J.. Robins · 2000
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“On Profile Likelihood: Comment”
James. Robins, Andrea Rotnitzky and Mark van Laan · 2000
Earlier work this paper cites.
“Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data”
Joseph D.. Kang and Joseph. Schafer · 2007
Earlier work this paper cites.
“Constructing inverse probability weights for marginal structural models.”
Stephen Cole and Miguel. Hern\’an · 2008
Earlier work this paper cites.
“Bayesian nonparametric modeling for causal inference”
Jennifer Hill · 2011
Earlier work this paper cites.
“Targeted Learning: Causal Inference for Observational and Experimental Data”, 2011
Mark van der Laan and Sherri Rose · 2011
Cited alongside, same era.
“Counterfactual reasoning and learning systems”
L\’eon Bottou et al · 2012
Cited alongside, same era.
“Practices and impact of primary outcome adjustment in randomized controlled trials: meta-epidemiologic study”
Nazmus Saquib, Juliann Saquib and John P Ioannidis · 2013
Cited alongside, same era.
“Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”
Ross. Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik · 2014
Cited alongside, same era.
“Non-parametrics for Causal Inference”, https://github.com/vdorie/npci , 2016
Vincent Dorie · 2016
Cited alongside, same era.
“Deep Counterfactual Networks with Propensity-Dropout”
Ahmed. Alaa, Michael Weisz and Mihaela van Schaar · 2017
Later among the works it cites.
“Double/Debiased Machine Learning for Treatment and Structural parameters”
Victor Chernozhukov et al · 2017
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“Double/Debiased/Neyman Machine Learning of Treatment Effects”
Victor Chernozhukov et al · 2017
Later among the works it cites.
“Causal effect inference with deep latent-variable models”
Christos Louizos et al · 2017
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Max. Farrell, Tengyuan Liang and Sanjog Misra · 2018
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Fredrik. Johansson, Uri Shalit and David Sontag · 2016
Cited alongside, same era.
“Semiparametric theory and empirical processes in causal inference”
Edward. Kennedy · 2016
Cited alongside, same era.
“One-Step Targeted Minimum Loss-based Estimation Based on Universal Least Favorable One-Dimensional Submodels”
M van Laan and S Gruber · 2016
Cited alongside, same era.
“Estimating individual treatment effect: generalization bounds and algorithms”
Uri Shalit, Fredrik. Johansson and David Sontag · 2016
Cited alongside, same era.
“Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes”
Ahmed Alaa and Mihaela van Schaar · 2017
Cited alongside, same era.
Patrick Schwab, Lorenz Linhardt and Walter Karlen · 2018
Later among the works it cites.
“Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis”
Y. Shimoni, C. Yanover, E. Karavani and Y. Goldschmnidt · 2018
Later among the works it cites.
“GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets”
Jinsung Yoon, James Jordon and Mihaela van Schaar · 2018
Later among the works it cites.
“Using Embeddings to Correct for Unobserved Confounding in Networks”
Victor Veitch, Yixin Wang and David. Blei · 2019
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