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Automated versus do-it-yourself methods for causal inference: Lessons
  learned from a data analysis competition
v1v2v3v4v5 (latest)

Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

9 July 2017
Vincent Dorie
J. Hill
Uri Shalit
M. Scott
D. Cervone
    CML
ArXiv (abs)PDFHTML

Papers citing "Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition"

13 / 113 papers shown
Title
Machine learning in policy evaluation: new tools for causal inference
Machine learning in policy evaluation: new tools for causal inference
N. Kreif
K. DiazOrdaz
ELMCML
71
46
0
01 Mar 2019
Adversarial Balancing for Causal Inference
Adversarial Balancing for Causal Inference
Michal Ozery-Flato
Pierre Thodoroff
Matan Ninio
Michal Rosen-Zvi
T. El-Hay
CMLGAN
98
27
0
17 Oct 2018
A comparison of methods for model selection when estimating individual
  treatment effects
A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler
M. Baiocchi
Robert Tibshirani
N. Shah
CML
77
59
0
14 Apr 2018
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Xinkun Nie
Stefan Wager
CML
198
658
0
13 Dec 2017
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal
  Inference
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
Tianyu Wang
Cynthia Rudin
M. Usaid Awan
Yameng Liu
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
109
51
0
19 Jul 2017
Meta-learners for Estimating Heterogeneous Treatment Effects using
  Machine Learning
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
Sören R. Künzel
Jasjeet Sekhon
Peter J. Bickel
Bin Yu
CML
198
934
0
12 Jun 2017
Estimation and Inference of Heterogeneous Treatment Effects using Random
  Forests
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager
Susan Athey
SyDaCML
405
2,506
0
14 Oct 2015
Recursive Partitioning for Heterogeneous Causal Effects
Recursive Partitioning for Heterogeneous Causal Effects
Susan Athey
Guido Imbens
CML
285
1,442
0
05 Apr 2015
To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and
  Butterfly-Bias
To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias
Peng Ding
Luke W. Miratrix
CML
107
125
0
02 Aug 2014
OpenML: networked science in machine learning
OpenML: networked science in machine learning
Joaquin Vanschoren
Jan N. van Rijn
B. Bischl
Luís Torgo
FedMLAI4CE
193
1,334
0
29 Jul 2014
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation
  Distances
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Marco Cuturi
OT
222
4,303
0
04 Jun 2013
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
Judea Pearl
CML
93
179
0
15 Mar 2012
BART: Bayesian additive regression trees
BART: Bayesian additive regression trees
H. Chipman
E. George
R. McCulloch
177
1,800
0
19 Jun 2008
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