English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A classical problem in causal inference is that of matching, where treatmentunits need to be matched to control units based on covariate information. Inthis work, we propose a method that computes high quality almost-exact matchesfor high-dimensional categorical datasets. This method, called FLAME (FastLarge-scale Almost Matching Exactly), learns a distance metric for matchingusing a hold-out training data set. In order to perform matching efficientlyfor large datasets, FLAME leverages techniques that are natural for queryprocessing in the area of database management, and two implementations of FLAMEare provided: the first uses SQL queries and the second uses bit-vectortechniques. The algorithm starts by constructing matches of the highest quality(exact matches on all covariates), and successively eliminates variables inorder to match exactly on as many variables as possible, while stillmaintaining interpretable high-quality matches and balance between treatmentand control groups. We leverage these high quality matches to estimateconditional average treatment effects (CATEs). Our experiments show that FLAMEscales to huge datasets with millions of observations where existingstate-of-the-art methods fail, and that it achieves significantly betterperformance than other matching methods.

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference