The report is to review and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data . The main identification algorithm can be seen as a repeated application of the rules of $do$-calculus . We introduce our newly developed Python library and givesome usage examples . We then thoroughly study the .identification algorithms presented by Shpitser and Pearl in 2006, explainingour implementation in Python alongside. The main . implementation algorithm canbe seen as . repeated application . of the . rules of the $do$.-calculculus, and it . either returns an expression for the causal query from experimental .probabilities or fails to identify the causal effect, in which case the effect is non-identifiable. We introduce a newly developed python library and giveome . usage examples. We also introduce our . examples

Author(s) : Martí Pedemonte, Jordi Vitrià, Álvaro Parafita

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Keywords : causal - python - examples - identification - introduce -

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