Causal Inference Python Example

The effects of Kaizen can be predicted before implementation — Visualizing the "future difference" by integrating time-series forecasting and causal effects

This series, which began with 541,909 rows of purchase data, has finally reached its final installment. In the first installment, we viewed 4,338 customers as three "clusters" using RFM and K-Means.

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Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.

pathmc is a Python package for Bayesian path analysis and structural causal modeling. You write your causal assumptions as a small set of structural equations in a lavaan-inspired formula language, ...

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The effects of Kaizen can be predicted before implementation — Visualizing the "future difference" by integrating time-series forecasting and causal effects

Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.

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