LASSO-type instrumental variable selection methods with an application to Mendelian randomization
(2024) In Statistical Methods in Medical Research- Abstract
Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In... (More)
Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.
(Less)
- author
- Qasim, Muhammad LU ; Månsson, Kristofer and Balakrishnan, Narayanaswamy
- publishing date
- 2024-11-15
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Causal inference, instrumental variable, model selection, LASSO, jackknife, heteroscedasticity, C13, C26, C36
- in
- Statistical Methods in Medical Research
- publisher
- SAGE Publications
- external identifiers
-
- scopus:85209377064
- pmid:39544096
- ISSN
- 0962-2802
- DOI
- 10.1177/09622802241281035
- language
- English
- LU publication?
- no
- id
- 58cdfc39-d76f-4358-a804-6e35d648ca10
- date added to LUP
- 2025-01-20 12:38:46
- date last changed
- 2025-07-08 17:46:26
@article{58cdfc39-d76f-4358-a804-6e35d648ca10,
abstract = {{<p>Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.</p>}},
author = {{Qasim, Muhammad and Månsson, Kristofer and Balakrishnan, Narayanaswamy}},
issn = {{0962-2802}},
keywords = {{Causal inference; instrumental variable; model selection; LASSO; jackknife; heteroscedasticity; C13; C26; C36}},
language = {{eng}},
month = {{11}},
publisher = {{SAGE Publications}},
series = {{Statistical Methods in Medical Research}},
title = {{LASSO-type instrumental variable selection methods with an application to Mendelian randomization}},
url = {{http://dx.doi.org/10.1177/09622802241281035}},
doi = {{10.1177/09622802241281035}},
year = {{2024}},
}