Kernel-based covariate functional balancing for observational studies
| Publication Date: |
2018-03-06
|
|---|---|
| Publisher: |
Oxford University Press
|
| Print ISSN: |
0006-3444
|
| Electronic ISSN: |
1464-3510
|
| Topics: |
Biology
Mathematics
Medicine
|
| Published by: |
| _version_ | 1836398817140801536 |
|---|---|
| autor | Wong R, Chan K. |
| beschreibung | Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods. |
| citation_standardnr | 6180684 |
| datenlieferant | ipn_articles |
| feed_id | 3549 |
| feed_publisher | Oxford University Press |
| feed_publisher_url | http://global.oup.com/ |
| insertion_date | 2018-03-06 |
| journaleissn | 1464-3510 |
| journalissn | 0006-3444 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Oxford University Press |
| quelle | Biometrika |
| relation | https://academic.oup.com/biomet/article/105/1/199/4718066?rss=1 |
| search_space | articles |
| shingle_author_1 | Wong R, Chan K. |
| shingle_author_2 | Wong R, Chan K. |
| shingle_author_3 | Wong R, Chan K. |
| shingle_author_4 | Wong R, Chan K. |
| shingle_catch_all_1 | Kernel-based covariate functional balancing for observational studies Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods. Wong R, Chan K. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_2 | Kernel-based covariate functional balancing for observational studies Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods. Wong R, Chan K. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_3 | Kernel-based covariate functional balancing for observational studies Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods. Wong R, Chan K. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_catch_all_4 | Kernel-based covariate functional balancing for observational studies Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods. Wong R, Chan K. Oxford University Press 0006-3444 00063444 1464-3510 14643510 |
| shingle_title_1 | Kernel-based covariate functional balancing for observational studies |
| shingle_title_2 | Kernel-based covariate functional balancing for observational studies |
| shingle_title_3 | Kernel-based covariate functional balancing for observational studies |
| shingle_title_4 | Kernel-based covariate functional balancing for observational studies |
| timestamp | 2025-06-30T23:33:06.326Z |
| titel | Kernel-based covariate functional balancing for observational studies |
| titel_suche | Kernel-based covariate functional balancing for observational studies |
| topic | W SA-SP WW-YZ |
| uid | ipn_articles_6180684 |