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2003.01747
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Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Neural Information Processing Systems (NeurIPS), 2020
3 March 2020
Victor Veitch
A. Zaveri
CML
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Papers citing
"Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding"
21 / 21 papers shown
Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows
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Conditional Average Treatment Effect Estimation Under Hidden Confounders
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178
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14 Jun 2025
Spillover Detection for Donor Selection in Synthetic Control Models
Michael O'Riordan
Ciarán M. Gilligan-Lee
278
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17 Jun 2024
A/B testing under Interference with Partial Network Information
Shiv Shankar
Ritwik Sinha
Yash Chandak
Saayan Mitra
M. Fiterau
293
4
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16 Apr 2024
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
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Javier Abad
Konstantin Donhauser
Fanny Yang
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340
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06 Dec 2023
Confounding-Robust Policy Improvement with Human-AI Teams
Ruijiang Gao
Mingzhang Yin
699
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13 Oct 2023
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
International Conference on Machine Learning (ICML), 2023
Lucile Ter-Minassian
Oscar Clivio
Karla Diaz-Ordaz
R. Evans
Chris Holmes
313
3
0
26 Jun 2023
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
Neural Information Processing Systems (NeurIPS), 2023
Abhin Shah
Karthikeyan Shanmugam
Murat Kocaoglu
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185
8
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19 Jun 2023
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
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Aniket Pramanick
Yufang Hou
Saif M. Mohammad
Iryna Gurevych
286
11
0
22 May 2023
Non-parametric identifiability and sensitivity analysis of synthetic control models
CLEaR (CLEaR), 2023
Jakob Zeitler
Athanasios Vlontzos
Ciarán M. Gilligan-Lee
CML
311
9
0
18 Jan 2023
Quantitative probing: Validating causal models using quantitative domain knowledge
Journal of Causal Inference (JCI), 2022
Daniel Grünbaum
M. L. Stern
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234
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07 Sep 2022
Causal Inference from Small High-dimensional Datasets
Raquel Y. S. Aoki
Martin Ester
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195
4
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19 May 2022
Partial Identification of Dose Responses with Hidden Confounders
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Myrl G. Marmarelis
E. Haddad
Andrew Jesson
N. Jahanshad
Aram Galstyan
Greg Ver Steeg
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425
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24 Apr 2022
Partial Identification with Noisy Covariates: A Robust Optimization Approach
CLEaR (CLEaR), 2022
Wenshuo Guo
Mingzhang Yin
Yixin Wang
Michael I. Jordan
436
20
0
22 Feb 2022
Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts
CLEaR (CLEaR), 2022
Boshen Zhang
Jiayao Zhang
189
5
0
02 Feb 2022
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Social Science Research Network (SSRN), 2021
Victor Chernozhukov
Carlos Cinelli
Whitney Newey
Amit Sharma
Vasilis Syrgkanis
CML
515
61
0
26 Dec 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma
Vasilis Syrgkanis
Cheng Zhang
Emre Kıcıman
244
36
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27 Aug 2021
Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction
International Conference on Process Mining (ICPM), 2021
Z. Bozorgi
Irene Teinemaa
Marlon Dumas
M. Rosa
Artem Polyvyanyy
183
39
0
15 May 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
International Conference on Machine Learning (ICML), 2021
Andrew Jesson
Sören Mindermann
Y. Gal
Uri Shalit
CML
366
64
0
08 Mar 2021
Technology Readiness Levels for AI & ML
Alexander Lavin
Ajay Sharma
VLM
348
157
0
21 Jun 2020
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Vincent Dorie
J. Hill
Uri Shalit
M. Scott
D. Cervone
CML
914
327
0
09 Jul 2017
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