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causaleffect (version 1.3.15)

Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models

Description

Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) , an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) , and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) . All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) .

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install.packages('causaleffect')

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598

Version

1.3.15

License

GPL (>= 2)

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Last Published

July 14th, 2022

Functions in causaleffect (1.3.15)

verma.constraints

Find Verma constraints for a given graph
get.expression

Get the expression of a probability object
surrogate.outcome

Derive a formula for a causal effect using surrogate outcomes
transport

Derive a transport formula for a causal effect between two domains
parse.graphml

Prepare GraphML files for internal use
causal.effect

Identify a causal effect
meta.transport

Derive a transport formula for a causal effect between a target domain and multiple source domains
generalize

Derive a transport formula for a causal effect between a target domain and multiple source domains with limited experiments
recover

Recover a causal effect from selection bias
aux.effect

Identify a causal effect using surrogate experiments
causaleffect-package

Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models