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Meta-learners for Estimating Heterogeneous Treatment Effects using
  Machine Learning
v1v2v3v4v5v6 (latest)

Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2017
12 June 2017
Sören R. Künzel
Jasjeet Sekhon
Peter J. Bickel
Bin Yu
    CML
ArXiv (abs)PDFHTML

Papers citing "Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning"

50 / 435 papers shown
Orthogonal Representation Learning for Estimating Causal Quantities
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk
Dennis Frauen
Jonas Schweisthal
Stefan Feuerriegel
CMLOOD
626
7
0
10 Apr 2026
Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routing
Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routing
Yichi Zhang
Fangzheng Xie
Shu Yang
Chong Wu
201
0
0
24 Dec 2025
Boosted Control Functions: Distribution generalization and invariance in confounded models
Boosted Control Functions: Distribution generalization and invariance in confounded models
Nicola Gnecco
Jonas Peters
Sebastian Engelke
Niklas Pfister
309
2
0
24 Dec 2025
Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEE
Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEE
Felix Saretzky
Lucas Andersen
Thomas Engel
Fazel Ansari
CML
224
0
0
30 Nov 2025
A Benchmark of Causal vs. Correlation AI for Predictive Maintenance
A Benchmark of Causal vs. Correlation AI for Predictive Maintenance
Krishna Taduri
Shaunak Dhande
Giacinto Paolo
Saggese
Paul Smith
75
0
0
30 Nov 2025
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang
Zhong Li
M. Leeuwen
Saber Salehkaleybar
CML
194
0
0
25 Nov 2025
Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation
Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation
Chenhao Zhai
Chang Meng
Xueliang Wang
Shuchang Liu
Xiaolong Hu
Shisong Tang
Xiaoqiang Feng
Xiu Li
OffRL
350
2
0
24 Nov 2025
Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT
Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT
Y. Liu
Hao Li
Haiyu Xu
Baoqi Pei
Jiahao Wang
...
Zheqi He
JG Yao
Bowen Qin
Xi Yang
J. Zhang
OffRL
440
0
0
21 Nov 2025
The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inference
The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inference
Sairam S
Sara Girdhar
Shivam Soni
CML
289
0
0
17 Nov 2025
Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
Yuki Murakami
Takumi Hattori
Kohsuke Kubota
CMLBDL
190
0
0
12 Nov 2025
A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
Ruyue Zhang
Xiaopeng Ke
Ming Liu
Fangzhou Shi
Chang Men
Zhengdan Zhu
176
0
0
03 Nov 2025
Gaining Momentum: Uncovering Hidden Scoring Dynamics in Hockey through Deep Neural Sequencing and Causal Modeling
Gaining Momentum: Uncovering Hidden Scoring Dynamics in Hockey through Deep Neural Sequencing and Causal Modeling
Daniel Griffiths
Piper Moskow
63
0
0
01 Nov 2025
Assessment of the conditional exchangeability assumption in causal machine learning models: a simulation study
Assessment of the conditional exchangeability assumption in causal machine learning models: a simulation study
Gerard T. Portela
Jason B. Gibbons
Sebastian Schneeweiss
R. J. Desai
CML
185
0
0
30 Oct 2025
Estimating Treatment Effects in Networks using Domain Adversarial Training
Estimating Treatment Effects in Networks using Domain Adversarial Training
Daan Caljon
Jente Van Belle
Wouter Verbeke
CML
183
0
0
24 Oct 2025
No Intelligence Without Statistics: The Invisible Backbone of Artificial Intelligence
No Intelligence Without Statistics: The Invisible Backbone of Artificial Intelligence
Ernest Fokoué
AI4TS
116
0
0
22 Oct 2025
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
Konstantin Hess
Dennis Frauen
M. Schaar
Stefan Feuerriegel
148
5
0
22 Oct 2025
Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data
Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data
Shuli Zhang
Hao Zhou
Jiaqi Zheng
Guibin Jiang
Bing Cheng
Wei Lin
Guihai Chen
OffRL
172
0
0
22 Oct 2025
Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
Harry Amad
Zhaozhi Qian
Dennis Frauen
Julianna Piskorz
Stefan Feuerriegel
Mihaela van der Schaar
CML
280
3
0
21 Oct 2025
A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
Jiayi Guo
Haoxuan Li
Ye Tian
Peng Wu
188
1
0
18 Oct 2025
Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems
Doubly Robust Estimation of Causal Effects in Strategic Equilibrium Systems
Sibo Xiao
159
0
0
17 Oct 2025
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
Valentyn Melnychuk
Dennis Frauen
Jonas Schweisthal
Stefan Feuerriegel
CML
232
1
0
29 Sep 2025
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Erdun Gao
Jake Fawkes
Dino Sejdinovic
CMLOOD
274
3
0
26 Sep 2025
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
Valentyn Melnychuk
Stefan Feuerriegel
133
0
0
26 Sep 2025
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Alejandro Almodóvar
Patricia A. Apellániz
Santiago Zazo
J. Parras
CML
365
1
0
26 Sep 2025
Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
Yuanchen Wu
Yubai Yuan
CML
316
1
0
23 Sep 2025
Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Zilong Wang
Turgay Ayer
Shihao Yang
CML
147
0
0
06 Sep 2025
Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
Amirmohammad Farzaneh
Matteo Zecchin
Osvaldo Simeone
193
0
0
04 Sep 2025
We Have It Covered: A Resampling-based Method for Uplift Model Comparison
We Have It Covered: A Resampling-based Method for Uplift Model Comparison
Yang Liu
Chaoyu Yuan
108
0
0
04 Sep 2025
Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Pracheta Amaranath
Vinitra Muralikrishnan
Amit Sharma
David D. Jensen
CMLELM
154
0
0
02 Sep 2025
ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
Jakob De Moor
Hans Weytjens
Johannes De Smedt
CML
118
0
0
31 Aug 2025
Counterfactual Probabilistic Diffusion with Expert Models
Counterfactual Probabilistic Diffusion with Expert Models
Wenhao Mu
Zhi Cao
Mehmed Uludag
Alexander Rodríguez
DiffM
262
1
0
18 Aug 2025
CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets
CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets
Gauri Jain
Dominik Rothenhäusler
Kirk Bansak
E. Paulson
173
0
0
15 Aug 2025
SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce
SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce
Li Kong
Bingzhe Wang
Zhou Chen
Suhan Hu
Yuchao Ma
Qi Qi
Suoyuan Song
Bicheng Jin
OffRL
181
0
0
08 Aug 2025
Multi-Treatment-DML: Causal Estimation for Multi-Dimensional Continuous Treatments with Monotonicity Constraints in Personal Loan Risk Optimization
Multi-Treatment-DML: Causal Estimation for Multi-Dimensional Continuous Treatments with Monotonicity Constraints in Personal Loan Risk Optimization
Kexin Zhao
Bo Wang
Cuiying Zhao
Tongyao Wan
214
0
0
04 Aug 2025
Structure Maintained Representation Learning Neural Network for Causal Inference
Structure Maintained Representation Learning Neural Network for Causal Inference
Yang Sun
Wenbin Lu
Yi-Hui Zhou
OODCML
162
0
0
03 Aug 2025
Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
Julianna Piskorz
Krzysztof Kacprzyk
Harry Amad
Mihaela van der Schaar
CML
225
0
0
09 Jul 2025
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
R. Karlsson
Piersilvio De Bartolomeis
Issa J. Dahabreh
Jesse H. Krijthe
CML
203
4
0
04 Jul 2025
LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Yuchen Ma
Dennis Frauen
Jonas Schweisthal
Stefan Feuerriegel
CML
257
4
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Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
Eric V. Strobl
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241
0
0
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Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
Zhongyuan Liang
L. Laan
Ahmed Alaa
280
1
0
16 Jun 2025
Foundation Models for Causal Inference via Prior-Data Fitted Networks
Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma
Dennis Frauen
Emil Javurek
Stefan Feuerriegel
CMLAI4CE
603
17
0
12 Jun 2025
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh
Hamidreza Kamkari
Valentin Thomas
Benson Li
Junwei Ma
Jesse C. Cresswell
Rahul G. Krishnan
CML
266
19
0
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Do-PFN: In-Context Learning for Causal Effect Estimation
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson
Arik Reuter
Siyuan Guo
Noah Hollmann
Katharina Eggensperger
Bernhard Schölkopf
CML
542
21
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A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
Yuchen Ma
Jonas Schweisthal
Hengrui Zhang
Stefan Feuerriegel
OODCML
206
3
0
02 Jun 2025
Doubly Robust Alignment for Large Language Models
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Erhan Xu
Kai Ye
Hongyi Zhou
Luhan Zhu
Francesco Quinzan
Chengchun Shi
360
11
0
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Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective
Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective
Dylan Zapzalka
Trenton Chang
Lindsay Warrenburg
Sae-Hwan Park
Daniel K. Shenfeld
Ravi B. Parikh
Jenna Wiens
Maggie Makar
183
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0
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TabPFN: One Model to Rule Them All?
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Qiong Zhang
Yan Shuo Tan
Qinglong Tian
Pengfei Li
451
12
0
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M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
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Xingyu Li
Qing Liu
Tony Jiang
Hong Amy Xia
Brian P. Hobbs
Peng Wei
328
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Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine
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Esmaeil Keyvanshokooh
Junyu Cao
312
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Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
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David I. Inouye
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427
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