This is the Linux app named DoWhy whose latest release can be downloaded as v0.13_GeneralizedAdjustmentCriterionforeffectestimationandmissingdatasupportinGCMsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
DoWhy
DESCRIPTION:
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes.
Features
- Examples available
- Documentation available
- Explicit identifying assumptions
- Separation between identification and estimation
- Automated validation of assumptions
- Default parameters for simple application of complex algorithms
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/dowhy.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.