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Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
Exact match - where data element linkages are made based on the exact name of a column in a database, the name of an XML element or a label on a screen. For example, if a database column has the name "PersonBirthDate" and a data element in a metadata registry also has the name "PersonBirthDate", automated tools can infer that the column of a database has the same semantics (meaning) as the ...
SPSS: A dialog box for Propensity Score Matching is available from the IBM SPSS Statistics menu (Data/Propensity Score Matching), and allows the user to set the match tolerance, randomize case order when drawing samples, prioritize exact matches, sample with or without replacement, set a random seed, and maximize performance by increasing ...
In data science, sameAs or exactMatch is a method of indicating that the subject of, or entity represented by, two resources is considered to be one and the same thing. It is a key part of the Semantic Web .
The case of exact graph matching is known as the graph isomorphism problem. [1] The problem of exact matching of a graph to a part of another graph is called subgraph isomorphism problem. Inexact graph matching refers to matching problems when exact matching is impossible, e.g., when the number of vertices in the two graphs are different. In ...
For companies, tech tools have improved the quality of data—but also made it easier to misinterpret and manipulate. Skip to main content. Sign in. Mail. 24/7 Help. For premium support please ...
This estimate can then be compared to the findings of observational research. Note that benchmarking is an attempt to calibrate non-statistical uncertainty (flaws in underlying assumptions). When combined with meta-analysis this method can be used to understand the scope of bias associated with a specific area of research.