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Kamat G, Reiter JP. Leveraging random assignment to impute missing covariates in causal studies. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1849217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Gauri Kamat
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Jerome P. Reiter
- Department of Statistical Science, Duke University, Durham, NC, USA
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Abstract
Summary
It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.
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Affiliation(s)
- S Yang
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina, U.S.A
| | - L Wang
- Department of Statistical Sciences, University of Toronto, 100 St. George Street, Toronto, Ontario, Canada
| | - P Ding
- Department of Statistics, University of California, 367 Evans Hall, Berkeley, California, U.S.A
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Jiang Z, Ding P. Using missing types to improve partial identification with application to a study of HIV prevalence in Malawi. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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