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causaleffect (version 1.3.12)

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

install.packages('causaleffect')

Monthly Downloads

424

Version

1.3.12

License

GPL (>= 2)

Maintainer

Santtu Tikka

Last Published

January 12th, 2021

Functions in causaleffect (1.3.12)

causal.effect

Identify a causal effect
generalize

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

Prepare GraphML files for internal use
surrogate.outcome

Derive a formula for a causal effect using surrogate outcomes
verma.constraints

Find Verma constraints for a given graph
transport

Derive a transport formula for a causal effect between two domains
recover

Recover a causal effect from selection bias
meta.transport

Derive a transport formula for a causal effect between a target domain and multiple source domains
get.expression

Get the expression of a probability object
causaleffect-package

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

Identify a causal effect using surrogate experiments