Package: causalnex
ThemeStatusLatest ReleasePython VersionmasterBranch BuilddevelopBranch BuildDocumentation BuildLicenseCode StyleWhat is CausalNex?"A toolkit for causal reasoning with Bayesian Networks."CausalNex aims to become one of the leading libraries for causal reasoning and "what-if" analysis using Bayesian Networks. It helps to simplify the steps:To learn causal structures,To allow domain experts to augment the relationships,To estimate the effects of potential interventions using data.Why CausalNex?CausalNex is built on our collective experience to leverage Bayesian Networks to identify causal relationships in data so that we can develop the right interventions from analytics. We developed CausalNex because:We believeleveraging Bayesian Networksis more intuitive to describe causality compared to traditional machine learning methodology that are built on pattern recognition and correlation analysis.Causal relationships are more accurate if we can easilyencode or augment domain expertisein the graph model.We can then use the graph model toassess the impactfrom changes to underlying features, i.e. counterfactual analysis, andidentify the right intervention.In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention.  CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis.What are the main features of CausalNex?The main features of this library are:Use state-of-the-art structure learning methods to understand conditional dependencies between variablesAllow domain knowledge to augment model relationshipBuild predictive models based on structural relationshipsFit probability distribution of the Bayesian NetworksEvaluate model quality with standard statistical checksSimplify how causality is understood in Bayesian Networks through visualisationAnalyse the impact of interventions using Do-calculusHow do I install CausalNex?CausalNex is a Python package. To install it, simply run:pipinstallcausalnexUseallfor a full installation of dependencies:pipinstall"causalnex[all]"See more detailed installation instructions, including how to setup Python virtual environments, in ourinstallation guideand get started with ourtutorial.How do I use CausalNex?You can find the documentation for the latest stable releasehere. It explains:An end-to-endtutorial on how to use CausalNexThemain concepts and methodsin using Bayesian Networks for Causal InferenceNote: You can find the notebook and markdown files used to build the docs indocs/source.Can I contribute?Yes! We'd love you to join us and help us build CausalNex. Check out ourcontributingdocumentation.How do I upgrade CausalNex?We useSemVerfor versioning. The best way to upgrade safely is to check ourrelease notesfor any notable breaking changes.How do I cite CausalNex?You may click "Cite this repository" under the "About" section of this repository to get the citation information in APA and BibTeX formats.What licence do you use?See ourLICENSEfor more detail.We're hiring!Do you want to be part of the team that builds CausalNex andother great productsat QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Machine Learning Engineers who love using data to drive their decisions. Take a look atour open positionsand see if you're a fit.