Fetching the paper…
Reading the bibliography…
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences.
Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes
Jerzy Neyman · 1923
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
D.B. Rubin · 1974
Earlier work this paper cites.
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.
Reducing bias in observational studies using subclassification on the propensity score
Paul R Rosenbaum and Donald B Rubin · 1984
Earlier work this paper cites.
Constructing a control group using multivariate matched sampling methods that incorporate the propensity score
Paul R Rosenbaum and Donald B Rubin · 1985
Earlier work this paper cites.
Statistics and causal inference
Paul Holland · 1986
Earlier work this paper cites.
Evaluating the econometric evaluations of training programs with experimental data
Robert J LaLonde · 1986
Earlier work this paper cites.
Model-based direct adjustment
Paul R Rosenbaum · 1987
Earlier work this paper cites.
Marginal structural models and causal inference in epidemiology, 2000
James M Robins, Miguel Angel Hernan, and Babette Brumback · 2000
Earlier work this paper cites.
Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders
M Soledad Cepeda, Ray Boston, John T Farrar, and Brian L Strom · 2003
Earlier work this paper cites.
Causal inference using potential outcomes
Donald Rubin · 2005
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Privacy-maintaining propensity score-based pooling of multiple databases applied to a study of biologics
Jeremy A Rassen, Daniel H Solomon, Jeffrey Curtis, Lisa Herrinton, and Sebastian Schneeweiss · 2010
Cited alongside, same era.
An introduction to propensity score methods for reducing the effects of confounding in observational studies
Peter C Austin · 2011
Cited alongside, same era.
Doubly Robust Policy Evaluation and Learning
Miroslav Dudík, John Langford, and Lihong Li · 2011
Cited alongside, same era.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Cited alongside, same era.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Cited alongside, same era.
The sum and difference of two lognormal random variables
Chi-Fai Lo · 2012
Counterfactual risk minimization: Learning from logged bandit feedback
Adith Swaminathan and Thorsten Joachims · 2015
Later among the works it cites.
Drawing causal inference from big data
Richard M. Shiffrin · 2016
Later among the works it cites.
Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
Later among the works it cites.
Private causal inference
Matt J. Kusner, Yu Sun, Karthik Sridharan, and Kilian Q. Weinberger · 2016
Later among the works it cites.
Tail behavior of sums and differences of log-normal random variables
Archil Gulisashvili, Peter Tankov, et al · 2016
Later among the works it cites.
Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, and Imed Zitouni · 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…
Cited alongside, same era.
Causal inference in public health
Thomas A. Glass, Steven N. Goodman, Miguel A. Hernàn, and Jonathan M. Samet · 2013
Cited alongside, same era.
Counterfactual reasoning and learning systems: The example of computational advertising
Léon Bottou, Jonas Peters, Joaquin Quiǹonero-Candela, Denis Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson · 2013
Cited alongside, same era.
Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data
Anand D. Sarwate and Kamalika Chaudhuri · 2013
Cited alongside, same era.
Concentration inequalities. A nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Guido W. Imbens and Donald B. Rubin · 2015
Cited alongside, same era.
Differential privacy preserving causal graph discovery
D. Xu, S. Yuan, and X. Wu · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
Later among the works it cites.
Ethics guidelines for trustworthy AI, 2019
High-level Expert Group on Artificial Intelligence · 2019
Closest in time.
cbps: Covariate Balancing Propensity Score
Christian Fong, Marc Ratkovic, Kosuke Imai, Chad Hazlett, Xiaolin Yang, and Sida Peng · 2019
Closest in time.
Causal Inference
Miguel A. Hernán and James M. Robins · 2019
Closest in time.