add community section, misc fixes (imgs, typos)

Signed-off-by: Fabio Vera <[email protected]>

Author: fverac

_includes/articles.html

@@ -40,13 +40,6 @@ <h2>{{ post.title }}</h2>
                             <p>
                                {{ excerpt }}
                             </p>
-                            {% if location %}
-                            <p>
-                                <a href="{{location}}" target="{{target}}">
-                                    Continue reading.
-                                </a> 
-                            </p>
-                            {% endif %}
                         </div>
                     </div>
                     <hr />

_includes/footer.html

@@ -2,7 +2,7 @@
   <div>
     <div>
       <a href="https://github.com/py-why/governance" target="_blank"
-        >Governancce</a
+        >Governance</a
       >
     </div>
   </div>

_includes/header.html

@@ -23,7 +23,7 @@ <h1>{{ site.title }}</h1>
           </ul>
         </li>
         <li><a href="news.html">News</a></li>
-        <li><a href="resources.html">Resources</a></li>
+        <li><a href="community.html">Community</a></li>
         <li><a href="https://github.com/py-why" target="_blank">GitHub</a></li>
       </ul>
     </nav>

_learn/01_introduction_to_dowhy.md

@@ -11,6 +11,13 @@ summary: >-
   refutation API that can automatically test causal assumptions for any estimation method, thus making inference more robust and 
   accessible to non-experts. DoWhy supports estimation of the average causal effect for backdoor, frontdoor, instrumental variable 
   and other identification methods, and estimation of the conditional effect (CATE) through an integration with the EconML library.
+
+  <br>
+  <br>
+  <a href="https://www.pywhy.org/dowhy">DoWhy Documentation</a>
+  <br>
+  <a href="https://github.com/py-why/dowhy">DoWhy GitHub Repository</a>
+  
 image: assets/dowhy-schematic.png
 image-alt: DoWhy | An end-to-end library for causal inference
 link: https://py-why.github.io/dowhy/

_learn/02_introduction_to_econml.md

@@ -1,12 +1,19 @@
 ---
-title: Econml | Automated Learning and Intelligence for Causation and Economics
+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. 
+  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.
+
+  <br>
+  <br>
+  <a href="https://econml.azurewebsites.net/">EconML Documentation</a>
+  <br>
+  <a href="https://github.com/py-why/EconML">EconML GitHub Repository</a>
+
 image: assets/econml-logo.png
-image-alt: Econml | Automated Learning and Intelligence for Causation and Economics
+image-alt: EconML | Automated Learning and Intelligence for Causation and Economics
 link: https://econml.azurewebsites.net/
 ---

_learn/03_general_tutorial_on_causal_inference.md

@@ -3,9 +3,13 @@ title: EconML | General Tutorial on Causal Inference
 slug: introduction-to-causal-inference
 layout: page
 description: >-
-  Causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs.
+  If you are new to causal inference, it may be helpful to walk through a quick overview of concepts and techniques that we refer to over the course of the documentation. We provide a high level introduction to causal inference tailored for EconML.
 summary: >-
-  Causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of Tetrad.
+  If you are new to causal inference, it may be helpful to walk through a quick overview of concepts and techniques that we refer to over the course of the documentation. We provide a high level introduction to causal inference tailored for EconML.
+  <br>
+  <br>
+  <a href="https://econml.azurewebsites.net/spec/causal_intro.html">Tutorial</a>
+
 image: assets/causal-inference.png
 image-alt: EconML | General Tutorial on Causal Inference
 link: https://econml.azurewebsites.net/spec/causal_intro.html

_learn/04_introduction_to_causal_learn.md

@@ -1,11 +1,14 @@
 ---
-title: Causal-learn | Causal Discovery for Python
+title: causal-learn | Causal Discovery for Python
 slug: introduction-to-causal-learn
 layout: page
 description: >-
-  Causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs.
+  causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs.
 summary: >-
-  Causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of Tetrad.
+  causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of Tetrad.
+  <br>
+  <br>
+  <a href="https://causal-learn.readthedocs.io/en/latest/">causal-learn Documentation</a>
 image: assets/causal-learn-logo.png
 image-alt: Causal-learn | Causal Discovery for Python
 link: https://causal-learn.readthedocs.io/en/latest/

assets/css/styles.scss

@@ -353,8 +353,8 @@ section {
 .card-header {
   height: 200px;
   background-repeat: no-repeat;
-  background-size: cover;
-  background-position: right bottom;
+  background-size: contain;
+  background-position: center;
   border-top-left-radius: $card-border-radius;
   border-top-right-radius: $card-border-radius;
 }
@@ -392,6 +392,7 @@ section {
   background-size: cover;
   background-position: right bottom;
   border-radius: $card-border-radius;
+  object-fit: contain;
 }
 
 /**

assets/econml-logo.png

@@ -0,0 +1,9 @@
+---
+layout: page
+---
+
+## Community
+
+PyWhy also has a [Discord](https://discord.gg/cSBGb3vsZb), which serves as a space for like-minded casual machine learning researchers and practitioners of all experience levels to come together to ask and answer questions, discuss new features, and share ideas.
+
+We invite you to join us at regular office hours and community calls in the Discord.

community.md

@@ -1,6 +1,7 @@
 ---
 layout: page
 permalink: learn/developer-resources.html
+redirect_from: resources.html
 ---
 
 {% include articles.html collection="learn" %}