The designed bootstrap for causal inference in big observational data
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Publication:2063871
DOI10.1007/s42519-021-00213-zzbMath1476.62239OpenAlexW3202452811WikidataQ130421208 ScholiaQ130421208MaRDI QIDQ2063871
Publication date: 3 January 2022
Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s42519-021-00213-z
Applications of statistics to biology and medical sciences; meta analysis (62P10) Statistical aspects of big data and data science (62R07)
Uses Software
Cites Work
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- Matching methods for causal inference: a review and a look forward
- For objective causal inference, design trumps analysis
- Bootstrap methods: another look at the jackknife
- Choice as an alternative to control in observational studies. (With comments and a rejoinder).
- Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion)
- Matching methods for observational studies derived from large administrative databases
- Overlap in observational studies with high-dimensional covariates
- Recursive partitioning for heterogeneous causal effects
- Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
- Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
- Matched Sampling for Causal Effects
- On the Failure of the Bootstrap for Matching Estimators
- The central role of the propensity score in observational studies for causal effects
- Statistics and Causal Inference
- Bias Reduction Using Mahalanobis-Metric Matching
- Nonparametric standard errors and confidence intervals
- Multivariate Matching Methods That are Equal Percent Bias Reducing, I: Some Examples
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
- Causal Inference for Statistics, Social, and Biomedical Sciences
- What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum
- Design of observational studies
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