Merge pull request #26 from AlxdrPolyakov/main

Create a new case study for pywhy, causaltune, Targeting variants for maximum impact
py-why · Sep 4, 2024 · 71f25a9 · 71f25a9
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diff --git a/_case_studies/11_improving_business_metrics_for_better_impact.md b/_case_studies/11_improving_business_metrics_for_better_impact.md
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+---
+title: Improving business metrics for better impact using the CausalTune library
+layout: page
+description: >-
+  This tutorial provides an introduction to improving business metrics using the ERUPT metric and the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric for optimizing clickthrough rates.
+summary: >-
+  This tutorial provides an introduction to improving business metrics using the ERUPT metric and the CausalTune library in Python. It shows a practical example and the use of the ERUPT metric for optimizing clickthrough rates. 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: Improving business metrics for better impact
+link: https://towardsdatascience.com/targeting-variants-for-maximum-impact-bdf26213d7bc
+---
diff --git a/assets/causaltune-targeting.png b/assets/causaltune-targeting.png