Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

5 October 2018

Papers citing "Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding"

22 / 22 papers shown

Title
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner Valentyn Melnychuk Stefan Feuerriegel M. Schaar CML 54 2 0 05 Nov 2024
Stabilized Neural Prediction of Potential Outcomes in Continuous Time Konstantin Hess Stefan Feuerriegel 48 0 0 04 Oct 2024
Conformal Counterfactual Inference under Hidden Confounding Zonghao Chen Ruocheng Guo Jean-François Ton Yang Liu CML OffRL 47 2 0 20 May 2024
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
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
Diagnosing Model Performance Under Distribution Shift Tiffany Cai Hongseok Namkoong Steve Yadlowsky 37 27 0 03 Mar 2023
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding Ashesh Rambachan Amanda Coston Edward H. Kennedy 11 4 0 19 Dec 2022
Sensitivity Analysis for Marginal Structural Models Matteo Bonvini Edward H. Kennedy V. Ventura Larry A. Wasserman CML 22 13 0 10 Oct 2022
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment Nathan Kallus 29 21 0 20 May 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 18 26 0 21 Apr 2022
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain David Bruns-Smith CML ELM OffRL 24 12 0 02 Apr 2022
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects Tobias Hatt Jeroen Berrevoets Alicia Curth Stefan Feuerriegel M. Schaar CML 52 29 0 25 Feb 2022
Long Story Short: Omitted Variable Bias in Causal Machine Learning Victor Chernozhukov Carlos Cinelli Whitney Newey Amit Sharma Vasilis Syrgkanis CML 18 35 0 26 Dec 2021
Towards Principled Causal Effect Estimation by Deep Identifiable Models Pengzhou (Abel) Wu Kenji Fukumizu BDL OOD CML 27 3 0 30 Sep 2021
Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable Shuxiao Chen B. Zhang 24 19 0 15 Apr 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding Andrew Jesson Sören Mindermann Y. Gal Uri Shalit CML 13 53 0 08 Mar 2021
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao Zuyue Fu Zhuoran Yang Yixin Wang Mladen Kolar Zhaoran Wang OffRL 18 34 0 19 Feb 2021
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
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding Hongseok Namkoong Ramtin Keramati Steve Yadlowsky Emma Brunskill OffRL 6 63 0 12 Mar 2020
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Nathan Kallus Angela Zhou OffRL 33 58 0 11 Feb 2020
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination Nathan Kallus Xiaojie Mao Angela Zhou FaML 24 155 0 01 Jun 2019