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Learning Representations for Counterfactual Inference

12 May 2016

Papers citing "Learning Representations for Counterfactual Inference"

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Title
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression Masahiro Kato Masaaki Imaizumi CML OOD 72 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 61 18 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
A Causal Lens for Controllable Text Generation Zhiting Hu Erran L. Li 109 64 0 22 Jan 2022
Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages Naveen Nair Karthik S. Gurumoorthy Dinesh Mandalapu CML 34 4 0 21 Jan 2022
DRTCI: Learning Disentangled Representations for Temporal Causal Inference Garima Gupta Lovekesh Vig Gautam M. Shroff BDL OOD CML 34 0 0 20 Jan 2022
Efficiently Disentangle Causal Representations Yuanpeng Li Joel Hestness Mohamed Elhoseiny Liang Zhao Kenneth Church OOD CML 29 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 53 23 0 05 Jan 2022
Deep Treatment-Adaptive Network for Causal Inference Qian Li Zhichao Wang Shaowu Liu Gang Li Guandong Xu CML BDL OOD 70 10 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 32 11 0 21 Dec 2021
Causal Knowledge Guided Societal Event Forecasting Songgaojun Deng Huzefa Rangwala Yue Ning AI4TS 61 2 0 10 Dec 2021
Enhancing Counterfactual Classification via Self-Training Ruijiang Gao Max Biggs Wei-Ju Sun Ligong Han CML OffRL 200 6 0 08 Dec 2021
Non parametric estimation of causal populations in a counterfactual scenario Céline Beji Florian Yger Jamal Atif CML OOD 32 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 101 14 0 07 Dec 2021
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies Milan Kuzmanovic Tobias Hatt Stefan Feuerriegel CML 94 22 0 06 Dec 2021
AI Assurance using Causal Inference: Application to Public Policy A. Svetovidov Abdul Rahman Feras A. Batarseh CML 31 2 0 01 Dec 2021
Loss Functions for Discrete Contextual Pricing with Observational Data Max Biggs Ruijiang Gao Wei-Ju Sun 198 10 0 18 Nov 2021
Causal Effect Variational Autoencoder with Uniform Treatment Daniel Jiwoong Im Kyunghyun Cho N. Razavian OOD CML BDL 30 9 0 16 Nov 2021
Variational Auto-Encoder Architectures that Excel at Causal Inference Negar Hassanpour Russell Greiner BDL CML 49 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 65 6 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 89 26 0 03 Nov 2021
Cycle-Balanced Representation Learning For Counterfactual Inference Guanglin Zhou L. Yao Xiwei Xu Chen Wang Liming Zhu CML OOD 38 12 0 29 Oct 2021
Extracting Expert's Goals by What-if Interpretable Modeling C. Chang George Adam Rich Caruana Anna Goldenberg OffRL 67 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 40 10 0 27 Oct 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Alicia Curth Changhee Lee M. Schaar CML 70 30 0 26 Oct 2021
Causal Effect Estimation using Variational Information Bottleneck Zhenyu Lu Yurong Cheng Mingjun Zhong G. Stoian Ye Yuan Guoren Wang CML 29 4 0 26 Oct 2021
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust Hossein Babaei Sina Alemohammad Richard Baraniuk 116 0 0 25 Oct 2021
$β$ -Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap Pengzhou (Abel) Wu Kenji Fukumizu CML 77 15 0 11 Oct 2021
Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou William E Carson IV David Carlson CML 208 8 0 04 Oct 2021
Towards Principled Causal Effect Estimation by Deep Identifiable Models Pengzhou (Abel) Wu Kenji Fukumizu BDL OOD CML 65 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 167 21 0 27 Sep 2021
Estimating Categorical Counterfactuals via Deep Twin Networks Athanasios Vlontzos Bernhard Kainz Ciarán M. Gilligan-Lee OOD CML BDL 113 17 0 04 Sep 2021
E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling Javier Albert Dmitri Goldenberg 52 25 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 70 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 49 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 50 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 107 38 0 13 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 81 111 0 02 Jul 2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge Abhin Shah Karthikeyan Shanmugam Kartik Ahuja CML 69 13 0 22 Jun 2021
Costs and Benefits of Fair Regression Han Zhao FaML 38 8 0 16 Jun 2021
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness Adam Foster Árpi Vezér C. A. Glastonbury Páidí Creed Sam Abujudeh Aaron Sim FaML 19 6 0 15 Jun 2021
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation Liyuan Xu Heishiro Kanagawa Arthur Gretton CML 75 37 0 07 Jun 2021
Counterfactual Maximum Likelihood Estimation for Training Deep Networks Xinyi Wang Wenhu Chen Michael Stephen Saxon Wenjie Wang OOD CML BDL 87 8 0 07 Jun 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 193 84 0 07 Jun 2021
Learning from Counterfactual Links for Link Prediction Tong Zhao Gang Liu Daheng Wang Wenhao Yu Meng Jiang CML OOD 87 100 0 03 Jun 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 259 51 0 03 Jun 2021
Causally motivated Shortcut Removal Using Auxiliary Labels Maggie Makar Ben Packer D. Moldovan Davis W. Blalock Yoni Halpern Alexander DÁmour OOD CML 74 75 0 13 May 2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments S Chandra Mouli Bruno Ribeiro OOD 77 12 0 20 Apr 2021
Sequential Deconfounding for Causal Inference with Unobserved Confounders Tobias Hatt Stefan Feuerriegel CML 93 29 0 16 Apr 2021