causalimpact

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

Some examples of using bsts (Bayesian Structural Time Series) to build causal impact models

A replication of the paper "Democratization and Economic Output in Sub-Saharan Africa".

An R package offering quick and easy prototyping for non-causal impact analysis.

Accurate Retail Sales Forecasting using Machine Learning, Causal Impact Analysis, and Explainable AI (SHAP). Full end-to-end pipeline for predicting Rossmann daily sales and quantifying promotion effects.

Prove your campaign caused the lift — not just that it correlated with it. A web dashboard that uses Bayesian Structural Time-Series (Google CausalImpact) to quantify the true causal effect of an intervention by reconstructing the counterfactual.

Causal Impact の理解と誤解

The Causal Impact model lets you examine ecommerce and marketing time series data to understand whether changes have led to a statistically significant performance improvement. Here's how to use PyCausalImpact to analyse changes in marketing activity or in this case on Boeing stock price

This analyses the impact of scandal impact on stock prices using the time series techniques.