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

dame-flame is a Python package for performing matching for observationalcausal inference on datasets containing discrete covariates. This packageimplements the Dynamic Almost Matching Exactly (DAME) and Fast Large-ScaleAlmost Matching Exactly (FLAME) algorithms, which match treatment and controlunits on subsets of the covariates. The resulting matched groups areinterpretable, because the matches are made on covariates, and high-quality,because machine learning is used to determine which covariates are important tomatch on. DAME solves an optimization problem that matches units on as manycovariates as possible, prioritizing matches on important covariates. FLAMEapproximates the solution found by DAME via a much faster backward featureselection procedure. The package provides several adjustable parameters toadapt the algorithms to specific applications, and can calculate treatmenteffect estimates after matching. Descriptions of these parameters, details onestimating treatment effects, and further examples, can be found in thedocumentation athttps://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference