Time Series Causal Impact Analysis in Python | Machine Learning 

`CausalImpact` package created by Google estimates the impact of an intervention on a time series. For example, how does a new feature on an application affect the users' time on the app?
In this tutorial, we will talk about how to use the Python package `CausalImpact` to do time series causal inference. You will learn:
👉 How to set the pre and post periods for the causal impact analysis?
👉 How to conduct causal inference on time series data?
👉 How to summarize the causality analysis results and create a report?
👉 What are the differences between the python and the R packages for `CausalImpact`?
⏰ Timecodes ⏰
0:00 - Intro
0:32 - Step 1: Install and Import Libraries
1:28 - Step 2: Create Dataset
3:02 - Step 3: Set Pre and Post Periods
3:54 - Step 4: Raw Differences
4:15 - Step 5: Causal Impact Analysis on Time Series
5:36 - Step 6: Time Series Causal Impact Summary
6:11 - Step 7: CausalImpact Package Differences between Python and R
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