Merge pull request #16 from py-why/emrekiciman-causaltune

Create 05_introduction_to_causaltune.md

_learn/05_introduction_to_causaltune.md

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+---
+title: CausalTune | A library for automated causal inference model estimation and selection
+slug: introduction-to-causaltune
+layout: page
+description: >-
+  An introduction to CausalTune, a Python library for automated tuning and selection of causal estimators. 
+summary: >-
+  CausalTune is a library for automated tuning and selection for causal estimators.
+  <br>
+  CausalTune enables automatic estimator tuning and selection by out-of-sample scoring of causal estimators, notably using the energy score.
+  We perform automated hyperparameter tuning of first stage models (for the treatment and outcome models) as well as hyperparameter tuning
+  and model selection for the second stage model (causal estimator).
+  Underlying estimators are taken from EconML, augmented by CausalTune, and called in a uniform fashion via a DoWhy wrapper.
+  We use FLAML for hyperparameter optimisation.
+  
+  <br>
+  <br>
+  <a href="https://www.pywhy.org/causaltune/">CausalTune Documentation</a>
+  <br>
+  <a href="https://github.com/py-why/causaltune">CausalTune GitHub Repository</a>
+  
+image: assets/pywhy-logo.png
+image-alt: CausalTune | A library for automated causal inference model estimation and selection
+link: https://www.pywhy.org/causaltune/
+---