Copertina

Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wu, Anpeng, Kuang, Kun, Xiong, Ruoxuan, Chen, Xiangwei, Sun, Zexu, Wu, Fei, Zhang, Kun
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

Causal Inference with Complex Treatments: A Survey
di: Wang, Yingrong, et al.
Pubblicazione: (2024)

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

Learning Individual Treatment Effects under Heterogeneous Interference in Networks
di: Zhao, Ziyu, et al.
Pubblicazione: (2022)

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

Advancing Counterfactual Inference through Nonlinear Quantile Regression
di: Xie, Shaoan, et al.
Pubblicazione: (2023)

Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
di: Shirani, Sadegh, et al.
Pubblicazione: (2025)

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
di: Chen, Hongyu, et al.
Pubblicazione: (2026)

Demystifying Spectral Feature Learning for Instrumental Variable Regression
di: Meunier, Dimitri, et al.
Pubblicazione: (2025)

Learning Treatment Representations for Downstream Instrumental Variable Regression
di: Lin, Shiangyi, et al.
Pubblicazione: (2025)

Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interference
di: Chen, Qianyi, et al.
Pubblicazione: (2026)

Semiparametric Counterfactual Regression
di: Kim, Kwangho
Pubblicazione: (2025)

Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
di: Wang, Yingrong, et al.
Pubblicazione: (2024)

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
di: Yin, Xiaohui, et al.
Pubblicazione: (2026)

Counterfactual Generative Models for Time-Varying Treatments
di: Wu, Shenghao, et al.
Pubblicazione: (2023)

M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
di: Sun, Zexu, et al.
Pubblicazione: (2024)

Distributional Instrumental Variable Method
di: Holovchak, Anastasiia, et al.
Pubblicazione: (2025)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2025)

Causal Contrastive Learning for Counterfactual Regression Over Time
di: Bouchattaoui, Mouad El, et al.
Pubblicazione: (2024)

Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness
di: Liang, Zihan, et al.
Pubblicazione: (2025)

Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
di: Liang, Zihan, et al.
Pubblicazione: (2026)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2026)

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
di: Wu, Dongze, et al.
Pubblicazione: (2025)

Generally-Occurring Model Change for Robust Counterfactual Explanations
di: Xu, Ao, et al.
Pubblicazione: (2024)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
di: Xie, Feng, et al.
Pubblicazione: (2023)

Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables
di: Apfel, Nicolas, et al.
Pubblicazione: (2021)

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
di: Komanduri, Aneesh, et al.
Pubblicazione: (2024)

Nonparametric Instrumental Variable Inference with Many Weak Instruments
di: van der Laan, Lars, et al.
Pubblicazione: (2025)

Conformal Prediction for Nonparametric Instrumental Regression
di: Kato, Masahiro
Pubblicazione: (2026)

Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method
di: Han, Kairong, et al.
Pubblicazione: (2025)

Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs
di: Xiong, Ruoxuan, et al.
Pubblicazione: (2024)

Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data
di: Chen, Jianmin, et al.
Pubblicazione: (2022)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
di: Zhang, Zhenhao, et al.
Pubblicazione: (2024)

Distributional Off-Policy Evaluation with Deep Quantile Process Regression
di: Kuang, Qi, et al.
Pubblicazione: (2026)

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
di: Zhu, Minqin, et al.
Pubblicazione: (2024)

DoubleGen: Debiased Generative Modeling of Counterfactuals
di: Luedtke, Alex, et al.
Pubblicazione: (2025)

Deep Limit Model-free Prediction in Regression
di: Wu, Kejin, et al.
Pubblicazione: (2024)

Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
di: Zhang, Yuling, et al.
Pubblicazione: (2024)

Conformalized Regression for Continuous Bounded Outcomes
di: Wu, Zhanli, et al.
Pubblicazione: (2025)

Mining Causality: AI-Assisted Search for Instrumental Variables
di: Han, Sukjin
Pubblicazione: (2024)

Identifiability of Sparse Causal Effects using Instrumental Variables
di: Pfister, Niklas, et al.
Pubblicazione: (2022)