Finding common support through largest connected components and its implementation

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Show simple item record Hongyuan, Lu 2019-04-18T15:27:59Z 2019-04-18T15:27:59Z 2019-05-01
dc.description.abstract In an observational study, the average treatment effect may only be reliably estimated for a subset of units under which the covariate space of both treatment and control units overlap. This is known as the common support assumption. In this report, we develop a method to find a region of common support. The method is as follows. Given a distance function to measure dissimilarity between any two units with differing treatment statuses, we can construct an adjacency list by drawing edges between each pair of treated and control units that have distance no larger than some pre-specified threshold. Then, all connected components of the graph are found. Finally, a region of common support is found by obtain- ing the largest connected components (LCC) (e.g. the connected components with the most treated units) of this graph. We implement the LCC algorithm by using binary search trees to find all the connected graphs from sample data and sorting them by size. This algorithm requires O(n²) runtime and O(n) memory (where n is the number of units in the observational study. We then create an R package implementing this LCC algorithm. Finally, we use our R package to compare the performance of LCC to that of other common support methods on simulated data. en_US
dc.language.iso en_US en_US
dc.subject Connected components en_US
dc.subject LCC en_US
dc.subject Observational study en_US
dc.subject Covariate en_US
dc.subject Propensity score en_US
dc.subject ATT en_US
dc.subject ATE en_US
dc.subject Treatment en_US
dc.subject Control en_US
dc.subject Treatment effect en_US
dc.title Finding common support through largest connected components and its implementation en_US
dc.type Report en_US Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Michael Higgins en_US 2019 en_US May en_US

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