Estimation of causal effects of binary treatments in unconfounded studies.

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Propensity score analysis with partially observed covariates: How should multiple imputation be used?Persistent pain after motor vehicle collision: comparative effectiveness of opioids vs nonsteroidal antiinflammatory drugs prescribed from the emergency department-a propensity matched analysis.

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P2860

Estimation of causal effects of binary treatments in unconfounded studies.

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Estimation of causal effects of binary treatments in unconfounded studies.

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Estimation of causal effects of binary treatments in unconfounded studies.

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Estimation of causal effects of binary treatments in unconfounded studies.

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P1476

Estimation of causal effects of binary treatments in unconfounded studies.

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P2093

Donald B Rubin

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Roee Gutman

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P2860

Matching methods for causal inference: A review and a look forward

Estimating causal effects of treatments in randomized and nonrandomized studies.

Inference and missing data

Divergence measures based on the Shannon entropy

Doubly robust estimation in missing data and causal inference models.

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.

Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate

Extensions of the penalized spline of propensity prediction method of imputation.

Model misspecification and robustness in causal inference: comparing matching with doubly robust estimation.

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3381-3398

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scholarly article

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10.1002/SIM.6532

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English

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2015-05-26T00:00:00Z

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26013308

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P932

4782596

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Estimation of causal effects of binary treatments in unconfounded studies.

about

P2860

P2860

Estimation of causal effects of binary treatments in unconfounded studies.

description

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P1476

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P2860

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P932