Causalnex Alternatives
Similar projects and alternatives to causalnex
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dowhy
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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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HumesGuillotine
Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
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genome_integration
MR-link and genome integration. genome_integration is a repository for the analysis of genomic data. Specifically, the repository implements the causal inference method MR-link, as well as other Mendelian randomization methods.
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auton-survival
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
causalnex reviews and mentions
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How many of you still buy and read textbooks after your degree?
I don't claim to defend that this is actually the right way of dealing with those things, but QuantumBlack gave a talk at neurips a couple years back, and really hyped up their package (https://github.com/quantumblacklabs/causalnex) for dealing with this stuff.
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What are some tools/best practices that Causal Inferencing teams use for experimentation?
As for causal libraries I'd recommend CausalNex, it's the only library that I know that does Judea Pearl's do() operator, and I think that's really great if you want to intervene over causal knowledge (that you'll want).
Stats
mckinsey/causalnex is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of causalnex is Python.
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