clean up news and case studies

Signed-off-by: kgao <[email protected]>

Author: kgao

_case_studies/05_hotel_booking_cancellations copy.md

@@ -0,0 +1,7 @@
+---
+title: Recommendation A/B testing
+description: "EconML’s DRIV estimator uses this experimental nudge to interpret experiments with imperfect compliance"
+image: assets/econml-ab.png
+image-alt: Recommendation A/B testing
+link: https://econml.azurewebsites.net/spec/motivation.html#recommendation-a-b-testing
+---

_case_studies/06_machine_learning_based_estimation_of_heterogeneous_treatment_effects.md

@@ -0,0 +1,7 @@
+---
+title: Customer Segmentation
+description: "EconML’s DML estimator uses price variations in existing data, estimates individualized responses to incentives."
+image: assets/customer-segmentation.png
+image-alt: Customer Segmentation
+link: https://econml.azurewebsites.net/spec/motivation.html#customer-segmentation
+---

_case_studies/06_recommendation_A_B_testing.md

@@ -0,0 +1,7 @@
+---
+title: Multi-investment Attribution
+description: "EconML’s Doubly Robust Learner model jointly estimates the effects of multiple discrete treatments."
+image: assets/multi-investment-attribution.png
+image-alt: Multi-investment Attribution
+link: https://econml.azurewebsites.net/spec/motivation.html#multi-investment-attribution
+---

_case_studies/07_customer_segmentation.md

@@ -3,7 +3,7 @@ title: Econml | Automated Learning and Intelligence for Causation and Economics
 slug: introduction-to-econml
 layout: page
 description: >-
-  An introduction to EconML,a project under Microsoft ALICE team effort to direct Artificial Intelligence towards economic decision making. We are building tools that combine state-of-the-art machine learning with econometrics – the measurement of economic systems — in order to bring automation to economic decision making. The heart of this project is a striving to measure causation: if you want to understand or make policy decisions in a complex economy, you need to know why the system moves the way it does.
+  An introduction to EconML,a project under Microsoft ALICE team effort to direct Artificial Intelligence towards economic decision making. 
 summary: >-
   EconML is a Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data. The suite of estimation methods provided in EconML represents the latest advances in causal machine learning. By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a broad set of users.
 image: assets/econml-logo.png

_case_studies/08_multi-investment_Attribution.md

@@ -1,11 +1,8 @@
 ---
 title: DoWhy evolves to independent PyWhy model to help causal inference grow
-description:
+description: Finding causal effects helps us learn about various phenomena in science and technology.
 summary: >-
-  Identifying causal effects is an integral part of scientific inquiry. It helps us
-  understand everything from educational outcomes to the effects of social policies
-  to risk factors for diseases. Questions of cause-and-effect are also critical for
-  the design and data-driven evaluation of many technological systems we build today.
+  Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven evaluation of many technological systems we build today.
 link: https://www.microsoft.com/en-us/research/blog/dowhy-evolves-to-independent-pywhy-model-to-help-causal-inference-grow/
 image: assets/pywhy-announcement.jpg
 image-alt: PyWhy

_learn/02_introduction_to_econml.md

@@ -1,7 +1,7 @@
 ---
 title: We're co-organizing a KDD workshop on causal inference and machine learning
 description: >-
-  Causal inference at scale presented at NABE.
+  Causal Inference and Machine Learning in Practice.
 summary: >-
   This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causal inference and machine learning techniques to real-world problems in the areas of product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable system design, algorithm bias, and interpretability.
 

_news/2022-05-31-dowhy-evolves-to-pywhy.md

@@ -94,15 +94,6 @@ To generate a local copy of the documentation from a clone of this repository, j
 
 The reStructuredText files that make up the documentation are stored in the [docs directory](https://github.com/py-why/EconML/tree/main/doc); module documentation is automatically generated by the Sphinx build process.
 
-## Release process
-
-We use GitHub Actions to build and publish the package and documentation.  To create a new release, an admin should perform the following steps:
-
-1. Update the version number in `econml/_version.py` and add a mention of the new version in the news section of this file and commit the changes.
-2. Manually run the publish_package.yml workflow to build and publish the package to PyPI.
-3. Manually run the publish_docs.yml workflow to build and publish the documentation.
-4. Under https://github.com/py-why/EconML/releases, create a new release with a corresponding tag, and update the release notes.
-
 <br/>
 <br/>
 <br/>