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

Our evolution as a species made a huge step forward when we understood therelationships between causes and effects. These associations may be trivial forsome events, but they are not in complex scenarios. To rigorously prove thatsome occurrences are caused by others, causal theory and causal inference wereformalized, introducing the $do$-operator and its associated rules. The maingoal of this report is to review and implement in Python some algorithms tocompute conditional and non-conditional causal queries from observational data.To this end, we first present some basic background knowledge on probabilityand graph theory, before introducing important results on causal theory, usedin the construction of the algorithms. We then thoroughly study theidentification algorithms presented by Shpitser and Pearl in 2006, explainingour implementation in Python alongside. The main identification algorithm canbe seen as a repeated application of the rules of $do$-calculus, and iteventually either returns an expression for the causal query from experimentalprobabilities or fails to identify the causal effect, in which case the effectis non-identifiable. We introduce our newly developed Python library and givesome usage examples.

Algorithmic Causal Effect Identification with causaleffect