`CausalImpact` package established by Google estimates the influence of an intervention on a time collection. For illustration, how does a new function on an software influence the users’ time on the application?
In this tutorial, we will communicate about how to use the Python package `CausalImpact` to do time collection causal inference. You will learn:
👉 How to established the pre and put up intervals for the causal impact examination?
👉 How to perform causal inference on time sequence info?
👉 How to summarize the causality investigation outcomes and generate a report?
👉 What are the variances amongst the python and the R packages for `CausalImpact`?
⏰ Timecodes ⏰
:00 – Intro
:32 – Phase 1: Install and Import Libraries
1:28 – Phase 2: Build Dataset
3:02 – Phase 3: Set Pre and Put up Periods
3:54 – Move 4: Raw Discrepancies
4:15 – Move 5: Causal Effects Investigation on Time Collection
5:36 – Stage 6: Time Collection Causal Influence Summary
6:11 – Move 7: CausalImpact Deal Differences involving Python and R
❤️ Weblog submit with code for this video: https://medium.com/grabngoinfo/time-sequence-causal-influence-examination-in-python-63eacb1df5cc
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