Efficient Randomized Algorithms for Large-scaled Exact Matchings with Multiple Controls: Implementation and Applications
Date Issued
2016
Date
2016
Author(s)
Hsu, Yu-Hsuan
Abstract
Matching is a common statistical method to estimate casual effects from observational data. And it is broadly used in various field. However, people usually are not aware the difference among the matching methods. In this paper, we briefly introduce various methods in matching nowadays. Then provide the new technology which uses network flow. The method has great advantage in ratio matching. The flow matching can support more ratio without dropping too much individuals. This phenomenon can lead to more accurate result. In summary, our method matches 20% more control candidates than the ones found using the traditional greedy method. Furthermore, the standard deviation of the Relative Risk factor found is also twice smaller than. In terms of the amount of improvements obtained in computing speed, our method is at least 7 times faster than a previous comparable study. We also compare with other matching methods in entropy to prove our method has enough randomness.
Subjects
matching
sample
flow
Type
thesis
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