Copertina

Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Peña, Jose M.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
di: Peña, Jose M.
Pubblicazione: (2024)

Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
di: Sethuraman, Muralikrishnna G., et al.
Pubblicazione: (2025)

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
di: Li, Yuhan, et al.
Pubblicazione: (2025)

Automating the Selection of Proxy Variables of Unmeasured Confounders
di: Xie, Feng, et al.
Pubblicazione: (2024)

Sequential Treatment Effect Estimation with Unmeasured Confounders
di: Wang, Yingrong, et al.
Pubblicazione: (2025)

Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
di: Boileau, Philippe, et al.
Pubblicazione: (2026)

Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows
di: Balgi, Sourabh, et al.
Pubblicazione: (2025)

Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework
di: Lin, Cheng, et al.
Pubblicazione: (2023)

Information-Theoretic Causal Bounds under Unmeasured Confounding
di: Jung, Yonghan, et al.
Pubblicazione: (2026)

Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
di: Wu, Yong, et al.
Pubblicazione: (2025)

$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows
di: Balgi, Sourabh, et al.
Pubblicazione: (2022)

Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
di: Zou, Yushu, et al.
Pubblicazione: (2025)

Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations
di: Freidling, Tobias, et al.
Pubblicazione: (2022)

The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding
di: Dalal, Abhinandan, et al.
Pubblicazione: (2026)

Sensitivity Analysis for Unmeasured Confounding in Medical Product Development and Evaluation Using Real World Evidence
di: Fang, Yixin, et al.
Pubblicazione: (2023)

Predictive Performance Comparison of Decision Policies Under Confounding
di: Guerdan, Luke, et al.
Pubblicazione: (2024)

Long-term Causal Inference Under Persistent Confounding via Data Combination
di: Imbens, Guido, et al.
Pubblicazione: (2022)

Addressing the Influence of Unmeasured Confounding in Observational Studies with Time-to-Event Outcomes: A Semiparametric Sensitivity Analysis Approach
di: Amoafo, Linda, et al.
Pubblicazione: (2024)

Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
di: Morinishi, Yoshimitsu, et al.
Pubblicazione: (2025)

SpaCE: The Spatial Confounding Environment
di: Tec, Mauricio, et al.
Pubblicazione: (2023)

Nonparametric Identification and Inference for Counterfactual Distributions with Confounding
di: Sun, Jianle, et al.
Pubblicazione: (2026)

Nonparametric Bounds in Causal Mediation Analysis in the Presence of Unmeasured Confounding and Imperfect Compliance
di: Liang, Wei, et al.
Pubblicazione: (2025)

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
di: Schröder, Maresa, et al.
Pubblicazione: (2023)

A Sensitivity Approach to Causal Inference Under Limited Overlap
di: Ma, Yuanzhe, et al.
Pubblicazione: (2025)

Causal Effect Identification in LiNGAM Models with Latent Confounders
di: Tramontano, Daniele, et al.
Pubblicazione: (2024)

Identification of Average Causal Effects in Confounded Additive Noise Models
di: Elahi, Muhammad Qasim, et al.
Pubblicazione: (2024)

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
di: Dharmakeerthi, Kulunu, et al.
Pubblicazione: (2024)

Causal Inference with Categorical Unobserved Confounder via Mixture Learning
di: Saha, Aytijhya, et al.
Pubblicazione: (2026)

Estimating Dyadic Treatment Effects with Unknown Confounders
di: Hoshino, Tadao, et al.
Pubblicazione: (2024)

Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
di: Lorbeer, Boris, et al.
Pubblicazione: (2024)

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
di: Schkoda, Daniela, et al.
Pubblicazione: (2024)

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
di: Hafer, Marlies, et al.
Pubblicazione: (2025)

DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confounding
di: Pal, Samhita, et al.
Pubblicazione: (2025)

Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models
di: Orihara, Shunichiro, et al.
Pubblicazione: (2023)

When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
di: Liu, Muye, et al.
Pubblicazione: (2025)

Multiple Regression Analysis of Unmeasured Confounding
di: Knaeble, Brian, et al.
Pubblicazione: (2025)

Auditing Fairness under Unobserved Confounding
di: Byun, Yewon, et al.
Pubblicazione: (2024)

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
di: Oprescu, Miruna, et al.
Pubblicazione: (2025)

A Look into How Machine Learning is Reshaping Engineering Models: the Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the Inevitable Rashomon Paradox
di: Naser, MZ
Pubblicazione: (2025)

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
di: Luo, Gongxu, et al.
Pubblicazione: (2025)