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Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

12 June 2017

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

50 / 191 papers shown

Title
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace Shu Wan Chen Zheng Zhonggen Sun Mengfan Xu Xiaoqing Yang Hong-Tu Zhu Jiecheng Guo 59 7 0 21 Mar 2022
Estimating the average causal effect of intervention in continuous variables using machine learning Yoshiaki Kitazawa CML 41 1 0 08 Mar 2022
Minimax rates for heterogeneous causal effect estimation Edward H. Kennedy Sivaraman Balakrishnan James M. Robins Larry A. Wasserman CML 93 31 0 02 Mar 2022
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
Differentially Private Estimation of Heterogeneous Causal Effects Fengshi Niu Harsha Nori B. Quistorff R. Caruana Donald Ngwe A. Kannan CML 93 14 0 22 Feb 2022
Validating Causal Inference Methods Harsh Parikh Carlos Varjao Louise Xu E. T. Tchetgen CML 87 21 0 09 Feb 2022
Generalized Causal Tree for Uplift Modeling Preetam Nandy Xiufan Yu Wanjun Liu Ye Tu Kinjal Basu S. Chatterjee CML 72 4 0 04 Feb 2022
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation Yi-Fan Zhang Hanlin Zhang Zachary Chase Lipton Li Erran Li Eric P. Xing OODD 115 30 0 02 Feb 2022
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance Gabriel Okasa CML 51 6 0 30 Jan 2022
Heterogeneous Peer Effects in the Linear Threshold Model Christopher Tran Elena Zheleva 96 13 0 27 Jan 2022
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling Eustache Diemert Artem Betlei Christophe Renaudin Massih-Reza Amini T. Gregoir Thibaud Rahier CML 71 10 0 19 Nov 2021
Loss Functions for Discrete Contextual Pricing with Observational Data Max Biggs Ruijiang Gao Wei-Ju Sun 198 10 0 18 Nov 2021
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects Steve Yadlowsky S. Fleming N. Shah Emma Brunskill Stefan Wager 55 65 0 15 Nov 2021
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects N. M. Kinyanjui Fredrik D. Johansson CML 45 0 0 12 Nov 2021
Interpretable Personalized Experimentation Han Wu S. Tan Weiwei Li Mia Garrard Adam Obeng Drew Dimmery Shaun Singh Hanson Wang Daniel R. Jiang E. Bakshy 65 6 0 05 Nov 2021
Selective Regression Under Fairness Criteria Abhin Shah Yuheng Bu Joshua K. Lee Subhro Das Yikang Shen P. Sattigeri G. Wornell 114 28 0 28 Oct 2021
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game Yuzi He Christopher Tran Julie Jiang Keith Burghardt Emilio Ferrara Elena Zheleva Kristina Lerman 45 10 0 27 Oct 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Alicia Curth Changhee Lee M. Schaar CML 77 30 0 26 Oct 2021
Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou William E Carson IV David Carlson CML 217 8 0 04 Oct 2021
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark Yaobin Ling Pulakesh Upadhyaya Luyao Chen Xiaoqian Jiang Yejin Kim CML 167 21 0 27 Sep 2021
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment Eli Ben-Michael D. J. Greiner Kosuke Imai Zhichao Jiang OffRL 229 22 0 22 Sep 2021
Machine learning reveals how personalized climate communication can both succeed and backfire Totte Harinen Alex Filipowicz Shabnam Hakimi Rumen Iliev M. Klenk Emily S. Sumner AI4CE 51 5 0 10 Sep 2021
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects Yao Zhang Jeroen Berrevoets M. Schaar CML 84 6 0 06 Aug 2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge Abhin Shah Karthikeyan Shanmugam Kartik Ahuja CML 69 13 0 22 Jun 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 193 84 0 07 Jun 2021
Learning Treatment Effects in Panels with General Intervention Patterns Vivek F. Farias Andrew A. Li Tianyi Peng CML 78 10 0 05 Jun 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 262 51 0 03 Jun 2021
Adaptive Multi-Source Causal Inference Thanh Vinh Vo Pengfei Wei T. Hoang Tze-Yun Leong 92 1 0 31 May 2021
Federated Estimation of Causal Effects from Observational Data Thanh Vinh Vo T. Hoang Young Lee Tze-Yun Leong FedML CML 70 13 0 31 May 2021
A Twin Neural Model for Uplift Mouloud Belbahri Olivier Gandouet A. Murua V. Nia CML 13 1 0 11 May 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park Uri Shalit Bernhard Schölkopf Krikamol Muandet CML 112 33 0 16 Feb 2021
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms Alicia Curth M. Schaar CML 181 149 0 26 Jan 2021
To do or not to do: cost-sensitive causal decision-making Diego Olaya Wouter Verbeke Jente Van Belle M. Guerry CML 63 4 0 05 Jan 2021
Teaching reproducible research for medical students and postgraduate pharmaceutical scientists A. Meid OnRL 28 0 0 07 Dec 2020
RealCause: Realistic Causal Inference Benchmarking Brady Neal Chin-Wei Huang Sunand Raghupathi CML ELM 72 34 0 30 Nov 2020
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions Yanbo Xu Divyat Mahajan Liz Manrao Amit Sharma Emre Kıcıman CML 37 2 0 11 Nov 2020
Interpretable Assessment of Fairness During Model Evaluation A. Sepehri Cyrus DiCiccio FaML 13 1 0 26 Oct 2020
Survey on Causal-based Machine Learning Fairness Notions K. Makhlouf Sami Zhioua C. Palamidessi FaML 151 85 0 19 Oct 2020
A Multi-resolution Theory for Approximating Infinite- $p$ -Zero- $n$ : Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Trade-off Xinran Li Xiangxu Meng 36 13 0 17 Oct 2020
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 94 56 0 14 Sep 2020
Sufficient Dimension Reduction for Average Causal Effect Estimation Debo Cheng Jiuyong Li Lin Liu Jixue Liu CML 51 15 0 14 Sep 2020
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs Z. Bozorgi Irene Teinemaa Marlon Dumas M. Rosa Artem Polyvyanyy CML AI4TS 38 45 0 03 Sep 2020
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 44 30 0 23 Aug 2020
Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints Dmitri Goldenberg Javier Albert Lucas Bernardi PabloA . Estevez 65 32 0 14 Aug 2020
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting Thibaud Rahier Amélie Héliou Matthieu Martin Christophe Renaudin Eustache Diemert 22 1 0 07 Aug 2020
Assessing External Validity Over Worst-case Subpopulations Sookyo Jeong Hongseok Namkoong 144 7 0 05 Jul 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 55 52 0 27 Jun 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 117 19 0 26 Jun 2020
Conformal Inference of Counterfactuals and Individual Treatment Effects Lihua Lei Emmanuel J. Candès CML 174 195 0 11 Jun 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid Loris Michel Jeffrey Näf N. Meinshausen Peter Buhlmann 96 43 0 29 May 2020