Image de couverture de livre

Leveraging specificity for causal inference in observational studies

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Miao, Wang
Format:	Preprint
Publié:	2025
Sujets:	Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Sample size and power calculations for causal inference of observational studies
par: Liu, Bo, et autres
Publié: (2025)

Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding
par: Lu, Sizhu, et autres
Publié: (2023)

Joint mixed-effects models for causal inference in clustered network-based observational studies
par: McNealis, Vanessa, et autres
Publié: (2024)

A confounding bridge approach for double negative control inference on causal effects
par: Miao, Wang, et autres
Publié: (2018)

On robust Bayesian causal inference
par: Alexopoulos, Angelos, et autres
Publié: (2025)

Leveraging text data for causal inference using electronic health records
par: Mozer, Reagan, et autres
Publié: (2023)

A cautionary note for plasmode simulation studies in the setting of causal inference
par: Shaw, Pamela A, et autres
Publié: (2025)

Post-selection inference for causal effects after causal discovery
par: Chang, Ting-Hsuan, et autres
Publié: (2024)

A doubly robust estimator for the Mann Whitney Wilcoxon Rank Sum Test when applied for causal inference in observational studies
par: Chen, Ruohui, et autres
Publié: (2024)

Design-based theory for causal inference
par: Lu, Xin, et autres
Publié: (2025)

An AI-powered Bayesian generative modeling approach for causal inference in observational studies
par: Liu, Qiao, et autres
Publié: (2025)

Combining observational and experimental data for causal inference considering data privacy
par: Mann, Charlotte Z., et autres
Publié: (2023)

Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations
par: Wang, Bingkai, et autres
Publié: (2024)

Robust Bayesian causal estimation for causal inference in medical diagnosis
par: Basu, Tathagata, et autres
Publié: (2024)

Inverse regression for causal inference with multiple outcomes
par: Zhang, Wei, et autres
Publié: (2025)

On causal inference with marked point process data
par: Ryalen, Pål Christie, et autres
Publié: (2026)

A causal inference framework for spatial confounding
par: Gilbert, Brian, et autres
Publié: (2021)

Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
par: Son, Dongjae, et autres
Publié: (2024)

A primer on optimal transport for causal inference with observational data
par: Gunsilius, Florian F
Publié: (2025)

Spatial causal inference in the presence of unmeasured confounding and interference
par: Papadogeorgou, Georgia, et autres
Publié: (2023)

Linear models for causal inference under network interference
par: Tong, Eric, et autres
Publié: (2026)

An exhaustive selection of sufficient adjustment sets for causal inference
par: Luo, Wei, et autres
Publié: (2024)

Credible causal inference beyond toy models
par: Bastías, Pablo Geraldo
Publié: (2024)

Dynamic causal inference with time series data
par: Schaffe-Odeleye, Tanique, et autres
Publié: (2026)

Collaborative causal inference on distributed data
par: Kawamata, Yuji, et autres
Publié: (2022)

Do-search -- a tool for causal inference and study design with multiple data sources
par: Karvanen, Juha, et autres
Publié: (2020)

On the limitations for causal inference in Cox models with time-varying treatment
par: Knudsen, Mark B., et autres
Publié: (2025)

Design-based theory for causal inference from adaptive experiments
par: Li, Xinran, et autres
Publié: (2026)

Re-examining and calibrating weighted survival analysis for causal inference
par: Xu, Wenfu, et autres
Publié: (2026)

Bidirectional causal inference for binary outcomes in the presence of unmeasured confounding
par: Deng, Yafang, et autres
Publié: (2026)

Disentangling spatial interference and spatial confounding biases in causal inference
par: Ogunsola, Isqeel, et autres
Publié: (2026)

Doubly-robust and heteroscedasticity-aware sample trimming for causal inference
par: Khan, Samir, et autres
Publié: (2022)

Practical considerations for Gaussian Process modeling for causal inference quasi-experimental studies with panel data
par: Vega, Sofia L., et autres
Publié: (2025)

Connections as treatment: causal inference with edge interventions in networks
par: Chen, Shuli, et autres
Publié: (2026)

Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference
par: Huang, Melody, et autres
Publié: (2025)

Prediction meets causal inference: the role of treatment in clinical prediction models
par: van Geloven, Nan, et autres
Publié: (2020)

Sample size and power calculations for causal inference with time-to-event outcomes
par: Yang, Chengxin, et autres
Publié: (2026)

General Bayesian inference for causal effects using covariate balancing procedure
par: Orihara, Shunichiro, et autres
Publié: (2024)

Design-based edge-level causal inference with machine learning assisted covariate adjustment
par: Yu, Haoyang, et autres
Publié: (2026)

A novel decomposition to explain heterogeneity in observational and randomized studies of causality
par: Gilbert, Brian, et autres
Publié: (2022)