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Estimation and Inference of Heterogeneous Treatment Effects using Random
  Forests
v1v2v3v4 (latest)

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

14 October 2015
Stefan Wager
Susan Athey
    SyDaCML
ArXiv (abs)PDFHTML

Papers citing "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests"

50 / 726 papers shown
Estimating Treatment Effect under Additive Hazards Models with
  High-dimensional Covariates
Estimating Treatment Effect under Additive Hazards Models with High-dimensional CovariatesJournal of the American Statistical Association (JASA), 2019
Jue Hou
Jelena Bradic
R. Xu
CML
122
1
0
29 Jun 2019
A Debiased MDI Feature Importance Measure for Random Forests
A Debiased MDI Feature Importance Measure for Random ForestsNeural Information Processing Systems (NeurIPS), 2019
Xiao Li
Yu Wang
Sumanta Basu
Karl Kumbier
Bin Yu
308
97
0
26 Jun 2019
Policy Targeting under Network Interference
Policy Targeting under Network InterferenceThe Review of Economic Studies (Rev. Econ. Stud.), 2019
Davide Viviano
858
41
0
24 Jun 2019
Analyzing CART
Analyzing CART
Jason M. Klusowski
556
6
0
24 Jun 2019
Linear Aggregation in Tree-based Estimators
Linear Aggregation in Tree-based EstimatorsJournal of Computational And Graphical Statistics (JCGS), 2019
Sören R. Künzel
Theo Saarinen
Edward W. Liu
Jasjeet Sekhon
420
12
0
15 Jun 2019
Identify treatment effect patterns for personalised decisions
Identify treatment effect patterns for personalised decisions
Jiuyong Li
Lin Liu
Yizhao Han
Saisai Ma
T. Le
Jixue Liu
CML
109
1
0
14 Jun 2019
Learning Individual Causal Effects from Networked Observational Data
Learning Individual Causal Effects from Networked Observational DataWeb Search and Data Mining (WSDM), 2019
Ruocheng Guo
Wenlin Yao
Huan Liu
CMLOOD
285
104
0
08 Jun 2019
Assessing Disparate Impacts of Personalized Interventions:
  Identifiability and Bounds
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Nathan Kallus
Angela Zhou
163
11
0
04 Jun 2019
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching
  Estimation in High Dimensions
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
Gentry Johnson
B. Quistorff
Matt Goldman
43
0
0
28 May 2019
Asymptotic Distributions and Rates of Convergence for Random Forests via
  Generalized U-statistics
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statisticsElectronic Journal of Statistics (EJS), 2019
Weiguang Peng
T. Coleman
L. Mentch
348
48
0
25 May 2019
Learning When-to-Treat Policies
Learning When-to-Treat PoliciesJournal of the American Statistical Association (JASA), 2019
Xinkun Nie
Emma Brunskill
Stefan Wager
CMLOffRL
262
98
0
23 May 2019
Experimental Evaluation of Individualized Treatment Rules
Experimental Evaluation of Individualized Treatment RulesJournal of the American Statistical Association (JASA), 2019
Kosuke Imai
Michael Lingzhi Li
297
45
0
14 May 2019
Adversarial Balancing-based Representation Learning for Causal Effect
  Inference with Observational Data
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational DataData mining and knowledge discovery (DMKD), 2019
Xin Du
Lei Sun
W. Duivesteijn
Alexander G. Nikolaev
Mykola Pechenizkiy
OODCML
185
49
0
30 Apr 2019
From Predictions to Prescriptions in Multistage Optimization Problems
From Predictions to Prescriptions in Multistage Optimization Problems
Dimitris Bertsimas
Christopher McCord
106
35
0
26 Apr 2019
Scalable and Efficient Hypothesis Testing with Random Forests
Scalable and Efficient Hypothesis Testing with Random Forests
T. Coleman
Wei Peng
L. Mentch
292
24
0
16 Apr 2019
Active Learning for Decision-Making from Imbalanced Observational Data
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin
Peter F. Schulam
E. Siivola
Aki Vehtari
Suchi Saria
Samuel Kaski
OODCML
185
30
0
10 Apr 2019
On nearly assumption-free tests of nominal confidence interval coverage
  for causal parameters estimated by machine learning
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu
Rajarshi Mukherjee
J. M. Robins
194
2
0
08 Apr 2019
Synthetic learner: model-free inference on treatments over time
Synthetic learner: model-free inference on treatments over time
Davide Viviano
Jelena Bradic
CML
316
21
0
02 Apr 2019
Machine Learning Methods Economists Should Know About
Machine Learning Methods Economists Should Know About
Susan Athey
Guido Imbens
257
775
0
24 Mar 2019
Unbiased Measurement of Feature Importance in Tree-Based Methods
Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhengze Zhou
Giles Hooker
568
77
0
12 Mar 2019
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
178
50
0
01 Mar 2019
Classifying Treatment Responders Under Causal Effect Monotonicity
Classifying Treatment Responders Under Causal Effect Monotonicity
Nathan Kallus
CML
288
16
0
14 Feb 2019
Weighted Tensor Completion for Time-Series Causal Inference
Weighted Tensor Completion for Time-Series Causal Inference
Debmalya Mandal
David C. Parkes
282
3
0
12 Feb 2019
Discovering Context Effects from Raw Choice Data
Discovering Context Effects from Raw Choice DataInternational Conference on Machine Learning (ICML), 2019
Arjun Seshadri
A. Peysakhovich
J. Ugander
174
28
0
08 Feb 2019
Modeling Heterogeneity in Mode-Switching Behavior Under a
  Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
Xilei Zhao
X. Yan
Pascal Van Hentenryck
87
11
0
08 Feb 2019
Learning Counterfactual Representations for Estimating Individual
  Dose-Response Curves
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab
Lorenz Linhardt
Stefan Bauer
J. M. Buhmann
W. Karlen
CMLOOD
218
146
0
03 Feb 2019
High-dimensional semi-supervised learning: in search for optimal
  inference of the mean
High-dimensional semi-supervised learning: in search for optimal inference of the mean
Yuqian Zhang
Jelena Bradic
142
35
0
02 Feb 2019
Time Series Deconfounder: Estimating Treatment Effects over Time in the
  Presence of Hidden Confounders
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden ConfoundersInternational Conference on Machine Learning (ICML), 2019
Ioana Bica
Ahmed Alaa
M. Schaar
BDLCMLAI4TS
502
128
0
01 Feb 2019
Learning Triggers for Heterogeneous Treatment Effects
Learning Triggers for Heterogeneous Treatment EffectsAAAI Conference on Artificial Intelligence (AAAI), 2019
Christopher Tran
Elena Zheleva
CML
267
25
0
31 Jan 2019
Personalized Treatment Selection using Causal Heterogeneity
Personalized Treatment Selection using Causal Heterogeneity
Ye Tu
Kinjal Basu
Cyrus DiCiccio
Jinyun Yan
B. Tiwana
Padmini Jaikumar
S. Chatterjee
CML
118
1
0
29 Jan 2019
Measuring Long-term Impact of Ads on LinkedIn Feed
Jinyun Yan
B. Tiwana
Souvik Ghosh
Haishan Liu
S. Chatterjee
55
6
0
29 Jan 2019
Inferring Heterogeneous Causal Effects in Presence of Spatial
  Confounding
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama
Dave Zachariah
Thomas B. Schon
156
8
0
28 Jan 2019
Learning Interpretable Models with Causal Guarantees
Learning Interpretable Models with Causal Guarantees
Carolyn Kim
Osbert Bastani
FaMLOODCML
144
18
0
24 Jan 2019
Non-Parametric Inference Adaptive to Intrinsic Dimension
Non-Parametric Inference Adaptive to Intrinsic Dimension
Khashayar Khosravi
Greg Lewis
Vasilis Syrgkanis
791
8
0
11 Jan 2019
Modified Causal Forests for Estimating Heterogeneous Causal Effects
Modified Causal Forests for Estimating Heterogeneous Causal Effects
M. Lechner
CML
191
51
0
22 Dec 2018
Likelihood Ratio Test in Multivariate Linear Regression: from Low to
  High Dimension
Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension
Yinqiu He
Tiefeng Jiang
Jiyang Wen
Gongjun Xu
280
13
0
17 Dec 2018
Consistent Estimation of Residual Variance with Random Forest Out-Of-Bag
  Errors
Consistent Estimation of Residual Variance with Random Forest Out-Of-Bag Errors
Burim Ramosaj
Markus Pauly
148
35
0
15 Dec 2018
Estimating Causal Effects With Partial Covariates For Clinical
  Interpretability
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
S. Parbhoo
Mario Wieser
Volker Roth
CML
76
0
0
26 Nov 2018
Reinforcement Learning for Uplift Modeling
Reinforcement Learning for Uplift Modeling
Chenchen Li
X. Yan
Xiaotie Deng
Yuan Qi
Wei Chu
Le Song
Junlong Qiao
Jianshan He
Junwu Xiong
OffRL
120
1
0
26 Nov 2018
Estimation of Individual Treatment Effect in Latent Confounder Models
  via Adversarial Learning
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Changhee Lee
Nicholas Mastronarde
M. Schaar
FedMLCML
125
20
0
21 Nov 2018
When do Words Matter? Understanding the Impact of Lexical Choice on
  Audience Perception using Individual Treatment Effect Estimation
When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation
Zhao Wang
A. Culotta
CML
210
16
0
12 Nov 2018
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison
  of Machine Learning and Logit Models
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Xilei Zhao
X. Yan
Alan Yu
Pascal Van Hentenryck
137
25
0
04 Nov 2018
Removing Hidden Confounding by Experimental Grounding
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus
N. Jethani
Uri Shalit
CML
240
157
0
27 Oct 2018
Adversarial Balancing for Causal Inference
Adversarial Balancing for Causal Inference
Michal Ozery-Flato
Pierre Thodoroff
Matan Ninio
Michal Rosen-Zvi
T. El-Hay
CMLGAN
222
27
0
17 Oct 2018
Bounding Optimality Gap in Stochastic Optimization via Bagging:
  Statistical Efficiency and Stability
Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability
Henry Lam
Huajie Qian
66
10
0
05 Oct 2018
Interval Estimation of Individual-Level Causal Effects Under Unobserved
  Confounding
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Nathan Kallus
Xiaojie Mao
Angela Zhou
CML
202
104
0
05 Oct 2018
Challenges of Using Text Classifiers for Causal Inference
Challenges of Using Text Classifiers for Causal Inference
Zach Wood-Doughty
I. Shpitser
Mark Dredze
CML
185
81
0
01 Oct 2018
Perfect Match: A Simple Method for Learning Representations For
  Counterfactual Inference With Neural Networks
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
Patrick Schwab
Lorenz Linhardt
W. Karlen
CMLBDL
328
119
0
01 Oct 2018
Deep Neural Networks for Estimation and Inference
Deep Neural Networks for Estimation and Inference
M. Farrell
Tengyuan Liang
S. Misra
BDL
401
260
0
26 Sep 2018
A Survey of Learning Causality with Data: Problems and Methods
A Survey of Learning Causality with Data: Problems and MethodsACM Computing Surveys (CSUR), 2018
Ruocheng Guo
Lu Cheng
Jundong Li
P. R. Hahn
Huan Liu
CML
355
179
0
25 Sep 2018
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