Binary Causality Inference Framework
adjustmentProb function
indpFunc function
bin2dec function
bIndpTest function
bSCMCausalGraphFunc function
bSCMdeConfoundingGraphFunc function
bSCMDepndentGraphFastFunc function
bSCMDepndentGraphFunc function
CausalGraphInferMainFunc function
comparePredAdjMatrix2TrueAdjMat
CondProb function
confNetFunc function
getReachableNodes function
getTransitiveClosureMat function
indpFunc function
num2Bits function
oddDiffFunc function
oddRatioFunc function
supp function
VecAlignment function
A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.