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Learning Representations for Counterfactual Inference

12 May 2016

Papers citing "Learning Representations for Counterfactual Inference"

50 / 414 papers shown

Title
Undersmoothing Causal Estimators with Generative Trees Damian Machlanski Spyridon Samothrakis Paul Clarke CML 134 1 0 16 Mar 2022
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials Zhixuan Chu S. Rathbun Sheng Li CML 82 10 0 10 Mar 2022
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation Kan Chen Qishuo Yin Q. Long CML 111 5 0 07 Mar 2022
Estimating Conditional Average Treatment Effects with Missing Treatment Information Milan Kuzmanovic Tobias Hatt Stefan Feuerriegel CML 103 7 0 02 Mar 2022
Neural Score Matching for High-Dimensional Causal Inference Oscar Clivio Fabian Falck B. Lehmann George Deligiannidis Chris Holmes CML 99 8 0 01 Mar 2022
Estimating causal effects with optimization-based methods: A review and empirical comparison Martin Cousineau V. Verter Susan Murphy J. Pineau CML 77 10 0 28 Feb 2022
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects Tobias Hatt Jeroen Berrevoets Alicia Curth Stefan Feuerriegel M. Schaar CML 136 35 0 25 Feb 2022
Ensemble Method for Estimating Individualized Treatment Effects K. Han Hanghao Wu CML FedML 56 5 0 25 Feb 2022
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data Zhixuan Chu S. Rathbun Sheng Li CML OOD 112 15 0 22 Feb 2022
Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez Sotirios A. Tsaftaris DiffM BDL 136 84 0 21 Feb 2022
Hierarchical Interpretation of Neural Text Classification Hanqi Yan Lin Gui Yulan He 159 14 0 20 Feb 2022
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression Masahiro Kato Masaaki Imaizumi CML OOD 121 1 0 10 Feb 2022
To Impute or not to Impute? Missing Data in Treatment Effect Estimation Jeroen Berrevoets F. Imrie T. Kyono James Jordon M. Schaar 147 19 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 165 31 0 02 Feb 2022
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance Gabriel Okasa CML 99 7 0 30 Jan 2022
A Causal Lens for Controllable Text Generation Zhiting Hu Erran L. Li 130 68 0 22 Jan 2022
Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages Naveen Nair Karthik S. Gurumoorthy Dinesh Mandalapu CML 68 4 0 21 Jan 2022
DRTCI: Learning Disentangled Representations for Temporal Causal Inference Garima Gupta Lovekesh Vig Gautam M. Shroff BDL OOD CML 56 0 0 20 Jan 2022
Efficiently Disentangle Causal Representations Yuanpeng Li Joel Hestness Mohamed Elhoseiny Liang Zhao Kenneth Church OOD CML 51 1 0 06 Jan 2022
BITES: Balanced Individual Treatment Effect for Survival data Stefan Schrod Andreas Schäfer S. Solbrig R. Lohmayer W. Gronwald P. Oefner T. Beissbarth Rainer Spang H. Zacharias Michael Altenbuchinger CML 77 24 0 05 Jan 2022
Deep Treatment-Adaptive Network for Causal Inference Qian Li Zhichao Wang Shaowu Liu Gang Li Guandong Xu CML BDL OOD 91 11 0 27 Dec 2021
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution Di Yao Chang Gong Lei Zhang Sheng Chen Jingping Bi CML 106 13 0 21 Dec 2021
Causal Knowledge Guided Societal Event Forecasting Songgaojun Deng Huzefa Rangwala Yue Ning AI4TS 86 2 0 10 Dec 2021
Enhancing Counterfactual Classification via Self-Training Ruijiang Gao Max Biggs Wei-Ju Sun Ligong Han CML OffRL 215 6 0 08 Dec 2021
Non parametric estimation of causal populations in a counterfactual scenario Céline Beji Florian Yger Jamal Atif CML OOD 42 0 0 08 Dec 2021
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time Jeroen Berrevoets Alicia Curth Ioana Bica E. McKinney M. Schaar CML BDL AI4CE 114 17 0 07 Dec 2021
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies Milan Kuzmanovic Tobias Hatt Stefan Feuerriegel CML 128 22 0 06 Dec 2021
AI Assurance using Causal Inference: Application to Public Policy A. Svetovidov Abdul Rahman Feras A. Batarseh CML 53 2 0 01 Dec 2021
Loss Functions for Discrete Contextual Pricing with Observational Data Max Biggs Ruijiang Gao Wei-Ju Sun 224 10 0 18 Nov 2021
Causal Effect Variational Autoencoder with Uniform Treatment Daniel Jiwoong Im Kyunghyun Cho N. Razavian OOD CML BDL 94 9 0 16 Nov 2021
Variational Auto-Encoder Architectures that Excel at Causal Inference Negar Hassanpour Russell Greiner BDL CML 61 3 0 11 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 91 7 0 05 Nov 2021
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization Renzhe Xu Xingxuan Zhang Zheyan Shen Tong Zhang Peng Cui OOD 196 30 0 03 Nov 2021
Cycle-Balanced Representation Learning For Counterfactual Inference Guanglin Zhou Weitong Chen Xiwei Xu Chen Wang Liming Zhu CML OOD 55 12 0 29 Oct 2021
Extracting Expert's Goals by What-if Interpretable Modeling C. Chang George Adam Rich Caruana Anna Goldenberg OffRL 105 0 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 61 10 0 27 Oct 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Alicia Curth Changhee Lee M. Schaar CML 101 31 0 26 Oct 2021
Causal Effect Estimation using Variational Information Bottleneck Zhenyu Lu Yurong Cheng Mingjun Zhong G. Stoian Ye Yuan Guoren Wang CML 60 4 0 26 Oct 2021
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust Hossein Babaei Sina Alemohammad Richard Baraniuk 161 0 0 25 Oct 2021
$β$ -Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap Pengzhou (Abel) Wu Kenji Fukumizu CML 113 15 0 11 Oct 2021
Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou William E Carson IV David Carlson CML 239 9 0 04 Oct 2021
Towards Principled Causal Effect Estimation by Deep Identifiable Models Pengzhou (Abel) Wu Kenji Fukumizu BDL OOD CML 144 3 0 30 Sep 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 241 22 0 27 Sep 2021
Estimating Categorical Counterfactuals via Deep Twin Networks Athanasios Vlontzos Bernhard Kainz Ciarán M. Gilligan-Lee OOD CML BDL 149 17 0 04 Sep 2021
E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling Javier Albert Dmitri Goldenberg 121 28 0 11 Aug 2021
The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions M. Rostami O. Saarela M. Escobar 101 1 0 02 Aug 2021
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators Alicia Curth M. Schaar CML 66 7 0 28 Jul 2021
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning Zhenyu Guo Shuai Zheng Zhizhe Liu Kun Yan Zhenfeng Zhu CML 84 16 0 19 Jul 2021
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition Junkun Yuan Anpeng Wu Kun Kuang Yangqiu Song Runze Wu Leilei Gan Lanfen Lin CML 135 40 0 13 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 147 117 0 02 Jul 2021