Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score Article Swipe
Muxuan Liang
,
Young‐Geun Choi
,
Yang Ning
,
Maureen A. Smith
,
Ying‐Qi Zhao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04445
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.04445
With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal utilizes the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/38098839
- OA Status
- green
- Cited By
- 1
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3041514166
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3041514166Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2007.04445Digital Object Identifier
- Title
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Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated scoreWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-07-08Full publication date if available
- Authors
-
Muxuan Liang, Young‐Geun Choi, Yang Ning, Maureen A. Smith, Ying‐Qi ZhaoList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/38098839Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2007.04445Direct OA link when available
- Concepts
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Estimator, Observational study, Propensity score matching, Inference, Covariate, Confidence interval, Statistics, Econometrics, Computer science, Causal inference, Statistical inference, Data mining, Nuisance parameter, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| primary_location.license | |
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| primary_location.landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38098839 |
| publication_date | 2020-07-08 |
| publication_year | 2020 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
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