Copertina

Learning treatment effects while treating those in need

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wilder, Bryan, Welle, Pim
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Methodology
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization
di: Lee, JungHo, et al.
Pubblicazione: (2026)

Accounting for Missing Covariates in Heterogeneous Treatment Estimation
di: Yamin, Khurram, et al.
Pubblicazione: (2024)

Orthogonal Causal Calibration
di: Whitehouse, Justin, et al.
Pubblicazione: (2024)

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

Detecting critical treatment effect bias in small subgroups
di: De Bartolomeis, Piersilvio, et al.
Pubblicazione: (2024)

Doubly robust identification of treatment effects from multiple environments
di: De Bartolomeis, Piersilvio, et al.
Pubblicazione: (2025)

Auditing Fairness by Betting
di: Chugg, Ben, et al.
Pubblicazione: (2023)

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
di: Kallus, Nathan, et al.
Pubblicazione: (2020)

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
di: Karlsson, Rickard, et al.
Pubblicazione: (2025)

Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
di: Sun, Yuming, et al.
Pubblicazione: (2026)

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

Estimation of conditional average treatment effects on distributed confidential data
di: Kawamata, Yuji, et al.
Pubblicazione: (2024)

Rate doubly robust estimation for weighted average treatment effects
di: Wang, Yiming, et al.
Pubblicazione: (2025)

Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
di: Pryce, Matthew, et al.
Pubblicazione: (2026)

Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
di: Chauhan, Vinod Kumar, et al.
Pubblicazione: (2023)

Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies
di: Son, Sungtaek, et al.
Pubblicazione: (2026)

Dynamic treatment effects: high-dimensional inference under model misspecification
di: Zhang, Yuqian, et al.
Pubblicazione: (2021)

Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments
di: Martin, Axel, et al.
Pubblicazione: (2024)

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
di: John, Luis H., et al.
Pubblicazione: (2020)

Causal effects based on distributional distances
di: Kim, Kwangho, et al.
Pubblicazione: (2018)

Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
di: Xia, Hongjing, et al.
Pubblicazione: (2023)

Dynamic covariate balancing: estimating treatment effects over time with potential local projections
di: Viviano, Davide, et al.
Pubblicazione: (2021)

Estimation of heterogeneous principal effects under principal ignorability
di: Zhang, Rui, et al.
Pubblicazione: (2026)

C-XGBoost: A tree boosting model for causal effect estimation
di: Kiriakidou, Niki, et al.
Pubblicazione: (2024)

A fast and effective kernel two-sample test for large-scale data
di: Song, Hoseung, et al.
Pubblicazione: (2021)

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
di: Nannapaneni, Saideep, et al.
Pubblicazione: (2025)

Interpretable Deep Learning Methods for Multiview Learning
di: Wang, Hengkang, et al.
Pubblicazione: (2023)

Doubly robust estimation of causal effects for random object outcomes with continuous treatments
di: Bhattacharjee, Satarupa, et al.
Pubblicazione: (2025)

Structured Credal Learning
di: Venkatesh, Varun, et al.
Pubblicazione: (2026)

i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
di: Ma, Chen, et al.
Pubblicazione: (2026)

Targeted Learning for Variable Importance
di: Wang, Xiaohan, et al.
Pubblicazione: (2024)

Discrete Causal Representation Learning
di: Zhang, Wenjin, et al.
Pubblicazione: (2026)

Learning to Partially Defer for Sequences
di: Rayan, Sahana, et al.
Pubblicazione: (2025)

Contrastive Network Representation Learning
di: Dong, Zihan, et al.
Pubblicazione: (2025)

Learning with the Nash-Sutcliffe loss
di: Tyralis, Hristos, et al.
Pubblicazione: (2026)

Federated Offline Reinforcement Learning
di: Zhou, Doudou, et al.
Pubblicazione: (2022)

Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
di: Shao, Daqian, et al.
Pubblicazione: (2025)

Interactive identification of individuals with positive treatment effect while controlling false discoveries
di: Duan, Boyan, et al.
Pubblicazione: (2021)

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
di: Zhou, Qin, et al.
Pubblicazione: (2026)

Identifiable causal inference with noisy treatment and no side information
di: Pöllänen, Antti, et al.
Pubblicazione: (2023)