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FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal
  Inference
v1v2v3v4v5v6v7v8v9 (latest)

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

19 July 2017
Tianyu Wang
Cynthia Rudin
M. Usaid Awan
Yameng Liu
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
ArXiv (abs)PDFHTML

Papers citing "FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference"

14 / 14 papers shown
Title
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation
Melanie F. Pradier
Javier González
CML
92
0
0
09 Sep 2024
An empirical process framework for covariate balance in causal inference
An empirical process framework for covariate balance in causal inference
Efrén Cruz-Cortés
K. Josey
Fan Yang
Debashis Ghosh
57
0
0
02 Jan 2023
Doubly Robust Kernel Statistics for Testing Distributional Treatment
  Effects
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
Jake Fawkes
Robert Hu
R. Evans
Dino Sejdinovic
OOD
61
3
0
09 Dec 2022
Estimating causal effects with optimization-based methods: A review and
  empirical comparison
Estimating causal effects with optimization-based methods: A review and empirical comparison
Martin Cousineau
V. Verter
Susan Murphy
J. Pineau
CML
40
9
0
28 Feb 2022
Validating Causal Inference Methods
Validating Causal Inference Methods
Harsh Parikh
Carlos Varjao
Louise Xu
E. T. Tchetgen
CML
75
21
0
09 Feb 2022
Interpretable Machine Learning: Fundamental Principles and 10 Grand
  Challenges
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaMLAI4CELRM
240
675
0
20 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence:
  Theory, Algorithms, and Applications
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Yu-Liang Chou
Catarina Moreira
P. Bruza
Chun Ouyang
Joaquim A. Jorge
CML
163
177
0
07 Mar 2021
dame-flame: A Python Library Providing Fast Interpretable Matching for
  Causal Inference
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Neha R. Gupta
Vittorio Orlandi
Chia-Rui Chang
Tianyu Wang
Marco Morucci
...
Xian Sun
Angikar Ghosal
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
CMLAI4CE
25
4
0
06 Jan 2021
Adaptive Hyper-box Matching for Interpretable Individualized Treatment
  Effect Estimation
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
Marco Morucci
Vittorio Orlandi
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
CML
50
15
0
03 Mar 2020
Almost-Matching-Exactly for Treatment Effect Estimation under Network
  Interference
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
M. Usaid Awan
Marco Morucci
Vittorio Orlandi
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
CML
75
13
0
02 Mar 2020
Interpretable Almost-Matching-Exactly With Instrumental Variables
Interpretable Almost-Matching-Exactly With Instrumental Variables
M. Usaid Awan
Yameng Liu
Marco Morucci
Sudeepa Roy
Cynthia Rudin
A. Volfovsky
47
5
0
27 Jun 2019
MALTS: Matching After Learning to Stretch
MALTS: Matching After Learning to Stretch
Harsh Parikh
Cynthia Rudin
A. Volfovsky
33
25
0
18 Nov 2018
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment
  Effect
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
Tong Wang
Cynthia Rudin
CMLBDL
85
27
0
16 Oct 2017
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
261
288
0
09 Jul 2017
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