Fetching the paper…
Reading the bibliography…
The paper proposes an estimator to make inference of heterogeneous treatment effects sorted by impact groups (GATES) for non-randomised experiments.
The sorted effects method: Discovering heterogeneous effects beyond their averages
Victor Chernozhukov, Iván Fernández-Val, and Ye Luo · 1938
Earlier work this paper cites.
A generalization of sampling without replacement from a finite universe
Daniel G Horvitz and Donovan J Thompson · 1952
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.
Root-n-consistent semiparametric regression
Peter M Robinson · 1988
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
James M Robins, Andrea Rotnitzky, and Lue Ping Zhao · 1994
Earlier work this paper cites.
Semiparametric efficiency in multivariate regression models with missing data
James M Robins and Andrea Rotnitzky · 1995
Earlier work this paper cites.
Characterizing selection bias using experimental data
James Heckman, Hidehiko Ichimura, Jeffrey Smith, and Petra Todd · 1998
Earlier work this paper cites.
Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study
Jared K Lunceford and Marie Davidian · 2004
Earlier work this paper cites.
The elements of statistical learning: data mining, inference, and prediction, Springer Series in Statistics
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Cited alongside, same era.
Inference on treatment effects after selection among high-dimensional controls
Alexandre Belloni, Victor Chernozhukov, and Christian Hansen · 2014
Cited alongside, same era.
Variance reduction in randomised trials by inverse probability weighting using the propensity score
Elizabeth J Williamson, Andrew Forbes, and Ian R White · 2014
Cited alongside, same era.
Doubly robust uniform confidence band for the conditional average treatment effect function
Sokbae Lee, Ryo Okui, and Yoon-Jae Whang · 2017
Cited alongside, same era.
Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2017
Cited alongside, same era.
Some methods for heterogeneous treatment effect estimation in high dimensions
Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H Shah, Trevor Hastie, and Robert Tibshirani · 2018
Later among the works it cites.
Generalized random forests
Susan Athey, Stefan Wager, and Julie Tibshirani · 2019
Closest in time.
Estimation of conditional average treatment effects with high-dimensional data
Qingliang Fan, Yu-Chin Hsu, Robert P Lieli, and Yichong Zhang · 2019
Closest in time.
Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
Closest in time.
A groupwise approach for inferring heterogeneous treatment effects in causal inference
Chan Park and Hyunseung Kang · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Victor Chernozhukov and Vira Semenova · 2018
Cited alongside, same era.
Heterogeneous treatment effects and optimal targeting policy evaluation
Günter J Hitsch and Sanjog Misra · 2018
Cited alongside, same era.
Machine learning estimation of heterogeneous causal effects: Empirical monte carlo evidence
Michael C Knaus, Michael Lechner, and Anthony Strittmatter · 2018
Cited alongside, same era.
Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins
Cited in the paper.
Generic machine learning inference on heterogenous treatment effects in randomized experiments
Victor Chernozhukov, Mert Demirer, Esther Duflo, and Ivan Fernandez-Val
Cited in the paper.
Michael Zimmert and Michael Lechner · 2019
Closest in time.
Double machine learning based program evaluation under unconfoundedness
Michael C Knaus · 2020
Closest in time.