Causal Graphs: Applying PyWhy to Go Beyond Explainability

Abstract: 

In a world obsessed with making predictions and generative AI, we often overlook the crucial task of making sense of these predictions and understanding results. We can't trust our decisions and policies if we don't know how and why recommendations are made. This session looks at using the PyWhy open-source ecosystem for causal machine learning and better decision-making.

In the realm of predictions, explainability, and causality, graphs have emerged as a potent model, leading to significant breakthroughs. These purposefully designed graphs capture and represent the intricate connections between entities, providing a comprehensive framework for understanding complex systems. Today, leading teams leverage this framework to surface directional patterns, compute complex logic, and as a foundation for causal inference.

This training will empower you by examining how to create and incorporate causal graphs into your predictive workflow to improve solutions. You'll gain a deep understanding of foundational concepts such as Jedeau Pearl's ""do"" operator, causal discovery, and how to keep domain expertise in the loop.

We'll delve into a practical example using PyWhy to evaluate city data and identify interventions that impact community resilience. We'll also explore using Causal Learn, LLMs, and other tools that expedite the complex process of modeling a problem as a causal graph.

Join us as we examine graphs' transformative potential and profound impact on predictive modeling, explainability, and causality in the era of generative AI. This is an exciting time for our field, and we're thrilled to share our insights with you.

Session Outline:

-Overview of PyWhy & DoWhy approach and available libraries
-Scenario: Estimating what has the biggest impact to city resilience using open city data.
-Python Notebook Walkthrough of the 4 Step DoWhy Process: Modeling Causality, Identification of Impact, Estimating Impact, Refuting Estimates.
----- Importance and Deep dive on first step of creating a causal graph.

- Understand PyWhy capabilities and how it can improve decision making as well as explainability.
- How to use of open source PyWhy libraries including DoWhy, Econ ML and Causal Learn.
- Learn different methods for generating casual graphs including LLMs and a new experimental tool.

Background Knowledge:

Python
Basic graph concepts

Review of PyWhy libraries recommended but not required:
https://www.pywhy.org/learn/developer-resources.html

Review of previous talk PDF recommended but not required: https://github.com/yulleyi/odsc_east_2024_graphs_explainability/tree/main

Bio: 

Michelle Yi is a technology leader that specializes in machine learning and cloud computing. She has 15 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves both on the board of Women in Data and as an avid volunteer for Girls Who Code.

Open Data Science

 

 

 

Open Data Science
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Cambridge, MA 02142
info@odsc.com

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