Fetching the paper…
Reading the bibliography…
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics.
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.
Evaluating the econometric evaluations of training programs with experimental data
Robert J LaLonde · 1986
Earlier work this paper cites.
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
Earlier work this paper cites.
The role of the propensity score in estimating dose-response functions
Guido W Imbens · 2000
Earlier work this paper cites.
Measuring living standards with proxy variables
Mark R Montgomery, Michele Gragnolati, Kathleen A Burke, and Edmundo Paredes · 2000
Earlier work this paper cites.
Identification and estimation of causal effects of multiple treatments under the conditional independence assumption
Michael Lechner · 2001
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
The propensity score with continuous treatments
Keisuke Hirano and Guido W Imbens · 2004
Earlier work this paper cites.
Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
Earlier work this paper cites.
Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference
Daniel E Ho, Kosuke Imai, Gary King, and Elizabeth A Stuart · 2007
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
BART: Bayesian additive regression trees
Hugh A Chipman, Edward I George, Robert E McCulloch, et al · 2010
Cited alongside, same era.
Doubly robust estimation of causal effects
Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, and Marie Davidian · 2011
Cited alongside, same era.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Cited alongside, same era.
MatchIt: nonparametric preprocessing for parametric causal inference
Daniel E Ho, Kosuke Imai, Gary King, Elizabeth A Stuart, et al · 2011
Cited alongside, same era.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
bartMachine: Machine learning with Bayesian additive regression trees
Estimating individual treatment effect: Generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
Later among the works it cites.
Recursive partitioning for personalization using observational data
Nathan Kallus · 2017
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2017
Later among the works it cites.
Deep counterfactual networks with propensity-dropout
Ahmed M Alaa, Michael Weisz, and Mihaela van der Schaar · 2017
Later among the works it cites.
Bayesian inference of individualized treatment effects using multi-task gaussian processes
Ahmed M Alaa and Mihaela van der Schaar · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam Kapelner and Justin Bleich · 2013
Cited alongside, same era.
Reputation and power: organizational image and pharmaceutical regulation at the FDA
Daniel Carpenter · 2014
Cited alongside, same era.
Assessing the Gold Standard — Lessons from the History of RCTs
Laura E. Bothwell, Jeremy A. Greene, Scott H. Podolsky, and David S. Jones · 2016
Cited alongside, same era.
BayesTree: Bayesian additive regression trees
Hugh Chipman and Robert McCulloch · 2016
Cited alongside, same era.
Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
Cited alongside, same era.
NPCI: Non-parametrics for causal inference, 2016
Vincent Dorie · 2016
Cited alongside, same era.
Susan Athey, Julie Tibshirani, and Stefan Wager · 2016
Cited alongside, same era.
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Later among the works it cites.
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
Later among the works it cites.
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
Closest in time.
Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Ahmed Alaa and Mihaela Schaar · 2018
Closest in time.
Importance sampling for minibatches
Dominik Csiba and Peter Richtárik · 2018
Closest in time.
A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah · 2018
Closest in time.