Installation
About
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.
Citation | BiCausality citation info |
github.com/DarkEyes/BiCausality | |
Bug report | File report |
Key Metrics
Downloads
Yesterday | 6 |
Last 7 days | 22 -44% |
Last 30 days | 217 -14% |
Last 90 days | 882 +7% |
Last 365 days | 3.138 +22% |