Estimation of local treatment effects under the binary instrumental variable model
| Autor: | Thomas S. Richardson, James M. Robins, Yuexia Zhang, Linbo Wang |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Estimation Applied Mathematics General Mathematics 05 social sciences Instrumental variable Binary number 01 natural sciences Agricultural and Biological Sciences (miscellaneous) Methodology (stat.ME) 010104 statistics & probability 0502 economics and business Statistics 0101 mathematics Statistics Probability and Uncertainty General Agricultural and Biological Sciences Statistics - Methodology 050205 econometrics Mathematics |
| Zdroj: | Biometrika. 108:881-894 |
| ISSN: | 1464-3510 0006-3444 |
| DOI: | 10.1093/biomet/asab003 |
| Popis: | Summary Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it can be more difficult than in the case with a continuous outcome. We propose novel modelling and estimation procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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