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# Introduction
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StructuralCausalModels.jl is part of the StatisticalRethinkingJulia eco system and, for now, containg functionality to analyse directed acyclic graph (DAG) based causal models as described in [StatisticalRethinking](https://xcelab.net/rm/statistical-rethinking/),[Causal Inference in Statistics](http://bcs.wiley.com/he-bcs/Books?action=index&bcsId=10288&itemId=1119186846) and [Cause and Correlation in Biology](https://www.cambridge.org/core/books/cause-and-correlation-in-biology/247799189B31939D24BC0F61FD59E9BB#).
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StructuralCausalModels.jl is part of the StatisticalRethinkingJulia eco system and contains functionality to analyse directed acyclic graph (DAG) based causal models as described in [StatisticalRethinking](https://xcelab.net/rm/statistical-rethinking/),[Causal Inference in Statistics](http://bcs.wiley.com/he-bcs/Books?action=index&bcsId=10288&itemId=1119186846) and [Cause and Correlation in Biology](https://www.cambridge.org/core/books/cause-and-correlation-in-biology/247799189B31939D24BC0F61FD59E9BB#).
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My initial goal for this package is to have a way to apply SCM ideas to the examples in [StatisticalRethinking.jl](https://github.com/StatisticalRethinkingJulia), i.e. a working version of `basis_set()`, `d_separation()` and `adjustment_sets()`.
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4. Sadeghi, K. (2011). Stable classes of graphs containing directed acyclic
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graphs, implementation as included in ggm.
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The latter two have been used for the Julia implementations of `basis_set()`, `d_separation()`, `shipley_test()`, `pcor_test()` and `ancestral_graph`.
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The latter two have been used for the Julia implementations of most fuctions
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in this package, e.g. `basis_set()`, `d_separation()`, `m_separation`,
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`shipley_test()`, `pcor_test()` and `ancestral_graph`.
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