Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

8 March 2021

Papers citing "Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding"

39 / 39 papers shown

Title
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective Hechuan Wen Tong Chen Mingming Gong Li Kheng Chai S. Sadiq Hongzhi Yin CML 58 0 0 08 May 2025
Your Assumed DAG is Wrong and Here's How To Deal With It Kirtan Padh Zhufeng Li Cecilia Casolo Niki Kilbertus CML 45 0 0 24 Feb 2025
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation Hechuan Wen Tong Chen Guanhua Ye Li Kheng Chai S. Sadiq Hongzhi Yin OOD 75 1 0 18 Nov 2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner Valentyn Melnychuk Stefan Feuerriegel M. Schaar CML 54 2 0 05 Nov 2024
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments J. Schweisthal Dennis Frauen M. Schaar Stefan Feuerriegel CML 45 3 0 04 Jun 2024
Causal Effect Estimation Using Random Hyperplane Tessellations Abhishek Dalvi Neil Ashtekar V. Honavar 48 0 0 16 Apr 2024
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials Piersilvio De Bartolomeis Javier Abad Konstantin Donhauser Fanny Yang CML 38 7 0 06 Dec 2023
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework Maresa Schröder Dennis Frauen Stefan Feuerriegel CML 32 6 0 30 Nov 2023
A Neural Framework for Generalized Causal Sensitivity Analysis Dennis Frauen F. Imrie Alicia Curth Valentyn Melnychuk Stefan Feuerriegel M. Schaar CML 31 10 0 27 Nov 2023
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel CML 32 9 0 19 Nov 2023
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation Konstantin Hess Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 27 14 0 26 Oct 2023
To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data Hechuan Wen Tong Chen Li Kheng Chai S. Sadiq Kai Zheng Hongzhi Yin CML 22 1 0 15 Sep 2023
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding Myrl G. Marmarelis Greg Ver Steeg Aram Galstyan Fred Morstatter CML OOD 16 5 0 15 Jun 2023
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 19 11 0 02 Jun 2023
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding Alizée Pace Hugo Yèche Bernhard Schölkopf Gunnar Rätsch Guy Tennenholtz OffRL 16 6 0 01 Jun 2023
Sharp Bounds for Generalized Causal Sensitivity Analysis Dennis Frauen Valentyn Melnychuk Stefan Feuerriegel CML 33 18 0 26 May 2023
Counterfactual Generative Models for Time-Varying Treatments Shenghao Wu Wen-liang Zhou Minshuo Chen Shixiang Zhu DiffM CML 36 6 0 25 May 2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding M. Oprescu Jacob Dorn Marah Ghoummaid Andrew Jesson Nathan Kallus Uri Shalit CML FedML 24 24 0 20 Apr 2023
Using uncertainty-aware machine learning models to study aerosol-cloud interactions Maelys Solal Andrew Jesson Y. Gal A. Douglas 16 0 0 30 Nov 2022
Normalizing Flows for Interventional Density Estimation Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 50 18 0 13 Sep 2022
Quantitative probing: Validating causal models using quantitative domain knowledge Daniel Grünbaum M. L. Stern E. Lang 20 5 0 07 Sep 2022
Estimating individual treatment effects under unobserved confounding using binary instruments Dennis Frauen Stefan Feuerriegel CML 21 17 0 17 Aug 2022
Prescriptive maintenance with causal machine learning Toon Vanderschueren R. Boute Tim Verdonck Bart Baesens Wouter Verbeke CML 14 1 0 03 Jun 2022
Causal Inference from Small High-dimensional Datasets Raquel Y. S. Aoki Martin Ester CML 24 4 0 19 May 2022
Towards assessing agricultural land suitability with causal machine learning Georgios Giannarakis Vasileios Sitokonstantinou R. Lorilla C. Kontoes CML 26 20 0 27 Apr 2022
Partial Identification of Dose Responses with Hidden Confounders Myrl G. Marmarelis E. Haddad Andrew Jesson N. Jahanshad Aram Galstyan Greg Ver Steeg CML 33 7 0 24 Apr 2022
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions Andrew Jesson A. Douglas P. Manshausen Maelys Solal N. Meinshausen P. Stier Y. Gal Uri Shalit CML 23 26 0 21 Apr 2022
Predicting the impact of treatments over time with uncertainty aware neural differential equations E. Brouwer J. Hernández Stephanie L. Hyland OOD CML 22 25 0 24 Feb 2022
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making S. Parbhoo Shalmali Joshi Finale Doshi-Velez ELM CML OffRL 11 5 0 20 Jan 2022
Long Story Short: Omitted Variable Bias in Causal Machine Learning Victor Chernozhukov Carlos Cinelli Whitney Newey Amit Sharma Vasilis Syrgkanis CML 24 35 0 26 Dec 2021
Causal Multi-Agent Reinforcement Learning: Review and Open Problems St John Grimbly Jonathan P. Shock Arnu Pretorius 44 17 0 12 Nov 2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data Andrew Jesson P. Tigas Joost R. van Amersfoort Andreas Kirsch Uri Shalit Y. Gal CML 44 30 0 03 Nov 2021
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific Andrew Jesson P. Manshausen A. Douglas D. Watson‐Parris Y. Gal P. Stier AI4Cl 15 6 0 28 Oct 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions Amit Sharma Vasilis Syrgkanis Cheng Zhang Emre Kıcıman 24 26 0 27 Aug 2021
Hölder Bounds for Sensitivity Analysis in Causal Reasoning Serge Assaad Shuxi Zeng H. Pfister Fan Li Lawrence Carin 14 0 0 09 Jul 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 177 50 0 03 Jun 2021
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
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015