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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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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.
I also have a series of blog posts on the topic: https://github.com/DataForScience/Causality where I work through Pearls Primer: https://amzn.to/3gsFlkO
I'm a fan of the Do Why library out of Microsoft. Even as a novice in the field of causal modeling it can get you up and running by estimating the causal graph based on your data. https://github.com/py-why/dowhy
Related posts
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Acceptable data formats for Predictive Stepwise Logistic Regression
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Do you use any specific framework when it comes to causal inference?
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Causal Explanations Considered Harmful: On the logical fallacy of causal projection
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[Q] What are some of the most useful topics/classes in philosophy for Statistics?
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[R] DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models