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

We show that if any number of variables are allowed to be simultaneously andindependently randomized in any one experiment, log2(N) + 1 experiments aresufficient and in the worst case necessary to determine the causal relationsamong N >= 2 variables when no latent variables, no sample selection bias andno feedback cycles are present. For all K, 0 < K < 1/(2N) we provide an upperbound on the number experiments required to determine causal structure wheneach experiment simultaneously randomizes K variables. For large N, thesebounds are significantly lower than the N - 1 bound required when eachexperiment randomizes at most one variable. For kmax < N/2, we show that(N/kmax-1)+N/(2kmax)log2(kmax) experiments aresufficient and in the worst casenecessary. We over a conjecture as to the minimal number of experiments thatare in the worst case sufficient to identify all causal relations among Nobserved variables that are a subset of the vertices of a DAG.

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables