Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Al...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2020-12-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2019-0024 |
| _version_ | 1818733523172851712 |
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| author | Corder Nathan Yang Shu |
| author_facet | Corder Nathan Yang Shu |
| author_sort | Corder Nathan |
| collection | DOAJ |
| description | The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation’s accuracy and precision with that of multiple imputation. |
| first_indexed | 2024-12-17T23:50:49Z |
| format | Article |
| id | doaj.art-35464a70e6d84b1bb6cca5bcc4985721 |
| institution | Directory Open Access Journal |
| issn | 2193-3677 2193-3685 |
| language | English |
| last_indexed | 2024-12-17T23:50:49Z |
| publishDate | 2020-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-35464a70e6d84b1bb6cca5bcc49857212022-12-21T21:28:12ZengDe GruyterJournal of Causal Inference2193-36772193-36852020-12-018124927110.1515/jci-2019-0024jci-2019-0024Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to MissingnessCorder Nathan0Yang Shu1North Carolina State UniversityNorth Carolina, USNorth Carolina State UniversityNorth Carolina, USThe problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation’s accuracy and precision with that of multiple imputation.https://doi.org/10.1515/jci-2019-0024missing datafractional imputationmultiple imputation62f1262f30 |
| spellingShingle | Corder Nathan Yang Shu Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness Journal of Causal Inference missing data fractional imputation multiple imputation 62f12 62f30 |
| title | Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness |
| title_full | Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness |
| title_fullStr | Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness |
| title_full_unstemmed | Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness |
| title_short | Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness |
| title_sort | estimating average treatment effects utilizing fractional imputation when confounders are subject to missingness |
| topic | missing data fractional imputation multiple imputation 62f12 62f30 |
| url | https://doi.org/10.1515/jci-2019-0024 |
| work_keys_str_mv | AT cordernathan estimatingaveragetreatmenteffectsutilizingfractionalimputationwhenconfoundersaresubjecttomissingness AT yangshu estimatingaveragetreatmenteffectsutilizingfractionalimputationwhenconfoundersaresubjecttomissingness |