Estimating causal effects with hidden confounding using instrumental variables and environments

DOI10.1214/23-ejs2160arXiv2207.14753OpenAlexW4388578568MaRDI QIDQ6184891

Min Jin Ha, James P. Long, Kim-Anh Do, Hongxu Zhu

Publication date: 5 January 2024

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.14753

zbMATH Keywords

instrumental variablescausal inferencehidden confoundingcausal Dantzig

Mathematics Subject Classification ID

Causal inference from observational studies (62D20)

Cites Work

• Large Sample Properties of Generalized Method of Moments Estimators
• Causal inference using invariant prediction: identification and confidence intervals
• Efficient estimation in models with independence restrictions
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• Adaptive estimation for some nonparametric instrumental variable models with full independence
• Inference for high-dimensional instrumental variables regression
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• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• Identification of Causal Effects Using Instrumental Variables
• The vec-permutation matrix, the vec operator and Kronecker products: a review
• Anchor Regression: Heterogeneous Data Meet Causality
• Invariant Causal Prediction for Sequential Data
• Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics
• Microeconometrics
• Unnamed Item

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