Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

14 October 2015

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

50 / 285 papers shown

Title
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond Amir Feder Katherine A. Keith Emaad A. Manzoor Reid Pryzant Dhanya Sridhar ... Roi Reichart Margaret E. Roberts Brandon M Stewart Victor Veitch Diyi Yang CML 58 235 0 02 Sep 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions Amit Sharma Vasilis Syrgkanis Cheng Zhang Emre Kıcıman 34 26 0 27 Aug 2021
Improving Inference from Simple Instruments through Compliance Estimation S. Coussens Jann Spiess CML 37 14 0 08 Aug 2021
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects Yao Zhang Jeroen Berrevoets M. Schaar CML 41 6 0 06 Aug 2021
The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions M. Rostami O. Saarela M. Escobar 53 1 0 02 Aug 2021
Federated Causal Inference in Heterogeneous Observational Data Ruoxuan Xiong Allison Koenecke Michael A. Powell Zhu Shen Joshua T. Vogelstein Susan Athey FedML CML 34 48 0 25 Jul 2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge Abhin Shah Karthikeyan Shanmugam Kartik Ahuja CML 45 13 0 22 Jun 2021
Learning Treatment Effects in Panels with General Intervention Patterns Vivek F. Farias Andrew A. Li Tianyi Peng CML 22 10 0 05 Jun 2021
Learning from Counterfactual Links for Link Prediction Tong Zhao Gang Liu Daheng Wang Wenhao Yu Meng Jiang CML OOD 40 96 0 03 Jun 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 185 50 0 03 Jun 2021
Stochastic Intervention for Causal Inference via Reinforcement Learning Tri Dung Duong Qian Li Guandong Xu CML 18 3 0 28 May 2021
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet FAtt 45 32 0 25 May 2021
The $s$ -value: evaluating stability with respect to distributional shifts Suyash Gupta Dominik Rothenhausler 44 16 0 07 May 2021
GEAR: On Optimal Decision Making with Auxiliary Data Hengrui Cai R. Song Wenbin Lu 60 1 0 21 Apr 2021
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters Carlos Fernández-Loría F. Provost CML 32 43 0 08 Apr 2021
Robust Orthogonal Machine Learning of Treatment Effects Yiyan Huang Cheuk Hang Leung Qi Wu Xing Yan OOD CML 24 0 0 22 Mar 2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments S. Parbhoo Stefan Bauer Patrick Schwab CML BDL 24 16 0 20 Mar 2021
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments Lizhen Nie Mao Ye Qiang Liu D. Nicolae CML 30 69 0 14 Mar 2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA Clément Bénard Sébastien Da Veiga Erwan Scornet 64 48 0 26 Feb 2021
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning L. Mentch Giles Hooker 23 9 0 23 Feb 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park Uri Shalit Bernhard Schölkopf Krikamol Muandet CML 44 31 0 16 Feb 2021
Estimating Average Treatment Effects via Orthogonal Regularization Tobias Hatt Stefan Feuerriegel CML 182 36 0 21 Jan 2021
Machine Learning Advances for Time Series Forecasting Ricardo P. Masini M. C. Medeiros Eduardo F. Mendes AI4TS 26 279 0 23 Dec 2020
Evaluating (weighted) dynamic treatment effects by double machine learning Hugo Bodory M. Huber Lukávs Lafférs CML 27 43 0 01 Dec 2020
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions Yanbo Xu Divyat Mahajan Liz Manrao Amit Sharma Emre Kıcıman CML 15 2 0 11 Nov 2020
On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries Irving Gómez-Méndez Émilien Joly 50 2 0 10 Nov 2020
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation Kristjan Greenewald Dmitriy A. Katz-Rogozhnikov Karthikeyan Shanmugam CML 53 9 0 03 Nov 2020
Double Robust Representation Learning for Counterfactual Prediction Shuxi Zeng Serge Assaad Chenyang Tao Shounak Datta Lawrence Carin Fan Li OOD CML 11 6 0 15 Oct 2020
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference A. Gentzel Purva Pruthi David D. Jensen CML 37 18 0 06 Oct 2020
GraphITE: Estimating Individual Effects of Graph-structured Treatments Shonosuke Harada H. Kashima CML 37 22 0 29 Sep 2020
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 33 52 0 14 Sep 2020
Sufficient Dimension Reduction for Average Causal Effect Estimation Debo Cheng Jiuyong Li Lin Liu Jixue Liu CML 24 14 0 14 Sep 2020
Machine Unlearning for Random Forests Jonathan Brophy Daniel Lowd MU 32 159 0 11 Sep 2020
Estimating Individual Treatment Effects with Time-Varying Confounders Ruoqi Liu Changchang Yin Ping Zhang CML 31 27 0 27 Aug 2020
Stochastic Optimization Forests Nathan Kallus Xiaojie Mao 37 48 0 17 Aug 2020
Estimating heterogeneous survival treatment effect in observational data using machine learning Liangyuan Hu Jiayi Ji Fan Li CML 38 63 0 17 Aug 2020
Modeling of time series using random forests: theoretical developments Richard A. Davis M. S. Nielsen AI4TS 19 16 0 06 Aug 2020
The foundations of cost-sensitive causal classification Wouter Verbeke Diego Olaya Jeroen Berrevoets Sam Verboven S. Maldonado CML 31 10 0 24 Jul 2020
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions Ahmed Alaa M. Schaar UD UQCV BDL TDI 32 53 0 29 Jun 2020
Learning Optimal Distributionally Robust Individualized Treatment Rules Weibin Mo Zhengling Qi Yufeng Liu 46 47 0 26 Jun 2020
Design and Evaluation of Personalized Free Trials Hema Yoganarasimhan E. Barzegary Abhishek Pani 15 11 0 24 Jun 2020
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions Ahmed Alaa M. Schaar UQCV BDL 24 22 0 20 Jun 2020
Conformal Inference of Counterfactuals and Individual Treatment Effects Lihua Lei Emmanuel J. Candès CML 28 190 0 11 Jun 2020
Sparse learning with CART Jason M. Klusowski 33 25 0 07 Jun 2020
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid Loris Michel Jeffrey Näf N. Meinshausen Peter Buhlmann 42 40 0 29 May 2020
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders Sorawit Saengkyongam Ricardo M. A. Silva CML 13 9 0 23 May 2020
Towards optimal doubly robust estimation of heterogeneous causal effects Edward H. Kennedy CML 38 314 0 29 Apr 2020
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects Will Y. Zou S. Shyam Michael Mui Mingshi Wang Jan Pedersen Zoubin Ghahramani CML 25 2 0 21 Apr 2020
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance L. Mentch Siyu Zhou 30 14 0 07 Mar 2020
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation Marco Morucci Vittorio Orlandi Sudeepa Roy Cynthia Rudin A. Volfovsky CML 16 15 0 03 Mar 2020