update

Signed-off-by: kgao <kevin.leo.gao@gmail.com>

Author: kgao

_case_studies/05_hotel_booking_cancellations copy.md

@@ -0,0 +1,7 @@
+---
+title: Hotel Booking Cancellations
+description: "Beyond predictive models: The causal story behind hotel booking cancellations."
+image: assets/hotel-booking-cancellations.png
+image-alt: Hotel Booking Cancellations
+link: https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf
+---

_case_studies/06_machine_learning_based_estimation_of_heterogeneous_treatment_effects.md

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+---
+title: Machine Learning Based Estimation of Heterogeneous Treatment Effects
+description: "The EconML package implements recent techniques in the literature at the intersection of econometrics and machine learning that tackle the problem of heterogeneous treatment effect estimation via machine learning based approaches. These novel methods offer large flexibility in modeling the effect heterogeneity (via techniques such as random forests, boosting, lasso and neural nets), while at the same time leverage techniques from causal inference and econometrics to preserve the causal interpretation of the learned model and many times also offer statistical validity via the construction of valid confidence intervals."
+image: assets/estimation_of_hte.png
+image-alt: Estimation of Heterogeneous Treatment Effects
+link: https://econml.azurewebsites.net/spec/motivation.html#motivating-examples
+---

_includes/header.html

@@ -24,7 +24,7 @@ <h1>{{ site.title }}</h1>
         </li>
         <li><a href="news.html">News</a></li>
         <li><a href="resources.html">Resources</a></li>
-        <li><a href="https://github.com/py-why/dowhy" target="_blank">GitHub</a></li>
+        <li><a href="https://github.com/py-why" target="_blank">GitHub</a></li>
       </ul>
     </nav>
   </header>

_learn/02_introduction_to_econml.md

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+---
+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.
+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
+image-alt: Econml | Automated Learning and Intelligence for Causation and Economics
+link: https://econml.azurewebsites.net/
+---
+
+

_learn/03_general_tutorial_on_causal_inference.md

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+---
+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.
+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.
+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

@@ -0,0 +1,14 @@
+---
+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.
+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.
+image: assets/causal-learn-logo.png
+image-alt: Causal-learn | Causal Discovery for Python
+link: https://causal-learn.readthedocs.io/en/latest/
+---
+
+

_news/2023-05-01-causal-inference-and-machine-learning-workshop

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+---
+title: We're co-organizing a KDD workshop on causal inference and machine learning
+description: >-
+  Causal inference at scale presented at NABE.
+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.
+  
+link: https://causal-machine-learning.github.io/kdd2023-workshop/ 
+image: assets/kdd-conf.png
+image-alt: Causal Inference and Machine Learning in Practice
+date: 2023-05-01
+---