Skip to content

Create a new case study for pywhy, causaltune, Targeting variants for maximum impact #26

New issue

Have a question about this project? # for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “#”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? # to your account

Merged
merged 8 commits into from
Sep 4, 2024
11 changes: 11 additions & 0 deletions _case_studies/11_targeting_variants_for_maximum_impact.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,11 @@
---
title: Targeting variants for maximum impact using the CausalTune library
layout: page
description: >-
This tutorial provides an introduction to causal AI using the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric.
summary: >-
This tutorial provides an introduction to causal AI using the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric. It also shows how to use ERUPT to evaluate previous experiments, as well as how to evaluate the potential effect of a future experiment with different assignments using a real business example.
image: assets/causaltune-targeting.png
image-alt: Targeting variants for maximum impact
link: https://towardsdatascience.com/targeting-variants-for-maximum-impact-bdf26213d7bc
---
Binary file added assets/causaltune-targeting.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.