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Causal Rule Ensemble: Interpretable Discovery and Inference of
  Heterogeneous Treatment Effects
v1v2v3v4v5v6 (latest)

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects

18 September 2020
Falco J. Bargagli-Stoffi
Riccardo Cadei
Kwonsang Lee
Francesca Dominici
    CML
ArXiv (abs)PDFHTML

Papers citing "Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects"

7 / 7 papers shown
Title
Benchmarking Heterogeneous Treatment Effect Models through the Lens of
  Interpretability
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Jonathan Crabbé
Alicia Curth
Ioana Bica
M. Schaar
CML
90
16
0
16 Jun 2022
Stable discovery of interpretable subgroups via calibration in causal
  studies
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi
Yan Shuo Tan
Briton Park
Mian Wei
Kevin Horgan
D. Madigan
Bin Yu
CML
42
30
0
23 Aug 2020
Automated versus do-it-yourself methods for causal inference: Lessons
  learned from a data analysis competition
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Vincent Dorie
J. Hill
Uri Shalit
M. Scott
D. Cervone
CML
193
288
0
09 Jul 2017
Explanation in Artificial Intelligence: Insights from the Social
  Sciences
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
252
4,281
0
22 Jun 2017
Generalized Random Forests
Generalized Random Forests
Susan Athey
J. Tibshirani
Stefan Wager
339
1,371
0
05 Oct 2016
Interpreting Tree Ensembles with inTrees
Interpreting Tree Ensembles with inTrees
Houtao Deng
103
246
0
23 Aug 2014
BART: Bayesian additive regression trees
BART: Bayesian additive regression trees
H. Chipman
E. George
R. McCulloch
158
1,796
0
19 Jun 2008
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