Causal Effect Inference with Deep Latent-Variable Models

24 May 2017

Joris Mooij

Papers citing "Causal Effect Inference with Deep Latent-Variable Models"

50 / 401 papers shown

Title
Invariant Representation Learning for Treatment Effect Estimation Claudia Shi Victor Veitch David M. Blei OOD CML 6 29 0 24 Nov 2020
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder Hyemi Kim Seungjae Shin Joonho Jang Kyungwoo Song Weonyoung Joo Wanmo Kang Il-Chul Moon BDL CML 6 57 0 24 Nov 2020
Balance Regularized Neural Network Models for Causal Effect Estimation Mehrdad Farajtabar Andrew Lee Yuanjian Feng Vishal Gupta Peter Dolan Harish Chandran M. Szummer CML 6 6 0 23 Nov 2020
A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus Xinpeng Shen Sisi Ma P. Vemuri Regina Castro P. Caraballo György J. Simon CML 11 1 0 11 Nov 2020
Incorporating Causal Effects into Deep Learning Predictions on EHR Data Jia Li Haoyu Yang X. Jia Vipin Kumar M. Steinbach György J. Simon BDL CML 28 1 0 11 Nov 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 12 108 0 04 Nov 2020
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation Kristjan Greenewald Dmitriy A. Katz-Rogozhnikov Karthikeyan Shanmugam CML 39 9 0 03 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 35 104 0 03 Nov 2020
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments Yuan Chen D. Zeng Tianchen Xu Yuanjia Wang AI4CE 9 3 0 30 Oct 2020
Counterfactual Representation Learning with Balancing Weights Serge Assaad Shuxi Zeng Chenyang Tao Shounak Datta Nikhil Mehta Ricardo Henao Fan Li Lawrence Carin CML OOD 12 63 0 23 Oct 2020
Poincare: Recommending Publication Venues via Treatment Effect Estimation Ryoma Sato M. Yamada H. Kashima CML 6 2 0 19 Oct 2020
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference A. Gentzel Purva Pruthi David D. Jensen CML 16 18 0 06 Oct 2020
Targeted VAE: Variational and Targeted Learning for Causal Inference M. Vowels Necati Cihan Camgöz Richard Bowden BDL OOD CML 11 8 0 28 Sep 2020
Sufficient Dimension Reduction for Average Causal Effect Estimation Debo Cheng Jiuyong Li Lin Liu Jixue Liu CML 11 14 0 14 Sep 2020
Adversarial Learning for Counterfactual Fairness Vincent Grari Sylvain Lamprier Marcin Detyniecki FaML 17 22 0 30 Aug 2020
Estimating Individual Treatment Effects with Time-Varying Confounders Ruoqi Liu Changchang Yin Ping Zhang CML 31 27 0 27 Aug 2020
Improving Fair Predictions Using Variational Inference In Causal Models Rik Helwegen Christos Louizos Patrick Forré FaML 19 6 0 25 Aug 2020
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution A. Khademi Vasant Honavar CML 12 9 0 01 Aug 2020
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders Andrew Bennett Nathan Kallus Lihong Li Ali Mousavi OffRL 35 43 0 27 Jul 2020
Causal Inference using Gaussian Processes with Structured Latent Confounders Sam Witty Kenta Takatsu David D. Jensen Vikash K. Mansinghka CML 13 19 0 14 Jul 2020
A unified survey of treatment effect heterogeneity modeling and uplift modeling Weijia Zhang Jiuyong Li Lin Liu CML 12 56 0 14 Jul 2020
On Linear Identifiability of Learned Representations Geoffrey Roeder Luke Metz Diederik P. Kingma CML 14 76 0 01 Jul 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Andrew Jesson Sören Mindermann Uri Shalit Y. Gal CML 14 72 0 01 Jul 2020
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data Sindy Lowe David Madras R. Zemel Max Welling CML BDL AI4TS 22 126 0 18 Jun 2020
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee Yao Zhang W. Zame Cong Shen Jang-Won Lee M. Schaar CML 6 18 0 14 Jun 2020
Structure by Architecture: Structured Representations without Regularization Felix Leeb Giulia Lanzillotta Yashas Annadani M. Besserve Stefan Bauer Bernhard Schölkopf OOD CML 46 8 0 14 Jun 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 28 178 0 11 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski Daniel Coelho De Castro Ben Glocker CML MedIm 33 229 0 11 Jun 2020
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Katherine A. Keith David D. Jensen Brendan O'Connor CML 12 112 0 01 May 2020
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks Ankit Sharma Garima Gupta Ranjitha Prasad Arnab Chatterjee L. Vig Gautam M. Shroff CML 21 7 0 28 Apr 2020
Causal Modeling with Stochastic Confounders Thanh Vinh Vo Pengfei Wei Wicher P. Bergsma Tze-Yun Leong BDL CML 11 0 0 24 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 12 2 0 21 Apr 2020
Estimating Individual Treatment Effects through Causal Populations Identification Céline Beji Michaël Bon Florian Yger Jamal Atif CML 6 3 0 10 Apr 2020
A theory of independent mechanisms for extrapolation in generative models M. Besserve Rémy Sun Dominik Janzing Bernhard Schölkopf 12 25 0 01 Apr 2020
Learning Latent Causal Structures with a Redundant Input Neural Network Jonathan D. Young Bryan Andrews G. Cooper Xinghua Lu CML 11 8 0 29 Mar 2020
A general framework for causal classification Jiuyong Li Weijia Zhang Lin Liu Kui Yu T. Le Jixue Liu CML 16 9 0 25 Mar 2020
ParKCa: Causal Inference with Partially Known Causes Raquel Y. S. Aoki Martin Ester CML 11 4 0 17 Mar 2020
Causal Inference With Selectively Deconfounded Data Kyra Gan Andrew A. Li Zachary Chase Lipton S. Tayur CML 4 7 0 25 Feb 2020
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models Imke Mayer Julie Josse Félix Raimundo Jean-Philippe Vert CML 29 12 0 25 Feb 2020
Towards unique and unbiased causal effect estimation from data with hidden variables Debo Cheng Jiuyong Li Lin Liu Kui Yu T. Le Jixue Liu CML 11 0 0 24 Feb 2020
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Nathan Kallus Angela Zhou OffRL 38 58 0 11 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
A Survey on Causal Inference Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao Aidong Zhang CML 18 498 0 05 Feb 2020
Treatment effect estimation with disentangled latent factors Weijia Zhang Lin Liu Jiuyong Li CML 18 88 0 29 Jan 2020
Causal query in observational data with hidden variables Debo Cheng Jiuyong Li Lin Liu Jixue Liu Kui Yu T. Le CML 18 11 0 28 Jan 2020
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson Uri Shalit Nathan Kallus David Sontag CML OOD 26 98 0 21 Jan 2020
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group Cédric Beaulac Jeffrey S. Rosenthal Q. Pei D. Friedman S. Wolden D. Hodgson 10 9 0 15 Jan 2020
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data Ruocheng Guo Jundong Li Huan Liu CML OffRL 30 21 0 22 Dec 2019
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation Zichen Zhang Qingfeng Lan Lei Ding Yue Wang Negar Hassanpour Russell Greiner BDL CML 22 9 0 19 Dec 2019
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population Ankit Sharma Garima Gupta Ranjitha Prasad Arnab Chatterjee L. Vig Gautam M. Shroff OOD 19 12 0 09 Dec 2019