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• W2890045984 abstract "Randomized clinical trials (RCTs) are ideal for estimating causal effects, because the distributions of background covariates are similar in expectation across treatment groups. When estimating causal effects using observational data, matching is a commonly used method to replicate the covariate balance achieved in a RCT. Matching algorithms have a rich history dating back to the mid-1900s, but have been used mostly to estimate causal effects between two treatment groups. When there are more than two treatments, estimating causal effects requires additional assumptions and techniques. We propose matching algorithms that address the drawbacks of the current methods, and we use simulations to compare current and new methods. All of the methods display improved covariate balance in the matched sets relative to the pre-matched cohorts. In addition, we provide advice to investigators on which matching algorithms are preferred for different covariate distributions." @default.
• W2890045984 created "2018-09-27" @default.
• W2890045984 creator A5046198636 @default.
• W2890045984 creator A5080669075 @default.
• W2890045984 date "2018-09-01" @default.
• W2890045984 modified "2023-09-24" @default.
• W2890045984 title "Matching Algorithms for Causal Inference with Multiple Treatments" @default.
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• W2890045984 hasPublicationYear "2018" @default.
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