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The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).
RE2 is a software library which implements a regular expression engine. It uses finite-state machines, in contrast to most other regular expression libraries.RE2 supports a C++ interface.
Moreover, even after an agent sees the final matching, they cannot deduce a strategy that would guarantee a better outcome in hindsight. This makes the Gale–Shapley algorithm a regret-free truth-telling mechanism. Moreover, in the Gale–Shapley algorithm, truth-telling is the only strategy that guarantees no regret.
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
Radius matching: all matches within a particular radius are used -- and reused between treatment units. Kernel matching: same as radius matching, except control observations are weighted as a function of the distance between the treatment observation's propensity score and control match propensity score. One example is the Epanechnikov kernel.
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The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects.
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related to: data matching strategy template google sheets