About

About Multiplicity: Roads Not Taken

Our Vision

Multiplicity: Roads Not Taken is an educational initiative dedicated to exploring the fundamental questions of and current developments in causal inference with a diverse audience. Our name draws inspiration from Robert Frost's poem, which captures the human curiosity about alternative paths — the "what ifs" that shape both personal journeys and scientific inquiry.

The Core Question

What would have happened if things had been different? This simple question lies at the heart of causal inference, scientific discovery, and human decision-making.

Our Mission

Why Causal Inference Matters

We believe that understanding cause and effect is essential for making informed decisions in research, policy, and everyday life. Yet, causal questions are inherently about counterfactuals: outcomes that were not observed, paths that were not taken.

1

Demystify causal inference

Provide clear, accessible educational resources that make complex concepts understandable

2

Bridge theory and practice

Offer concrete examples and implementation guidance with real code and data

3

Foster a community

Connect learners, researchers, and practitioners interested in causal methods

4

Explore philosophical dimensions

Examine the deeper implications of counterfactual thinking across disciplines

Educational Philosophy

A Layered Approach to Learning

We believe that effective education requires multiple entry points and reinforcement at different levels of abstraction. Our approach mirrors the scientific process itself: observation, hypothesis, testing, and interpretation.

1

Story First

Begin with relatable narratives that illustrate why causal questions matter in real-world contexts

2

Theory Second

Explain the mathematical and conceptual foundations clearly without unnecessary jargon

3

Practice Third

Provide hands-on examples with real code and data for immediate application

4

Reflection Last

Encourage critical thinking about assumptions, limitations, and ethical implications

Content Approach

Our Guiding Principles

All our content is crafted with careful attention to both substance and presentation. We believe that educational materials should be as rigorous as they are accessible.

A

Accessibility

Complex ideas explained in plain language without sacrificing precision

R

Rigor

Mathematically sound foundations presented without unnecessary jargon

P

Practicality

Real-world examples with implementable code in R and/or Python

T

Transparency

Clear discussion of assumptions, limitations, and methodological choices

I

Interdisciplinary

Drawing insights from economics, psychology, political science, and more

The Team

Feiran Zhang

Founder

As someone who keeps pondering about the "what-ifs", I started this Project as a personal endeavor out of my passion for both education and research, simply to share knowledge about causal inference with a wider audience.

Contact: zephyr.v@outlook.com

Collaboration Welcome

We are currently a solo project but actively welcome collaborations from educators, researchers, and developers interested in causal inference education. If you have ideas for content, teaching materials, or research applications, please reach out!

Acknowledgments

Gratitude & Inspiration

This project stands on the shoulders of giants and benefits from countless contributions from the academic and open-source communities.

Intellectual Foundations

We gratefully acknowledge the foundational work of Donald Rubin, James Robins, Judea Pearl, and others who developed the modern framework for causal inference that makes this educational mission possible.

Open Source Community

This project relies on tools like R, Python, Jekyll, and countless libraries maintained by volunteers worldwide. We are deeply grateful for their contributions.

Educators & Researchers

To the educators and researchers who have shared their knowledge freely through lectures, textbooks, blog posts, and open courses—your generosity inspires this project.

Literary Inspiration

The poem "The Road Not Taken" by Robert Frost continues to resonate with anyone who has wondered about alternative paths, reminding us that our choices shape both personal journeys and scientific inquiry.

Get Involved

Join Our Community

This project is built for and by the community. We believe that education thrives through collaboration, feedback, and shared exploration.

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Learn

Take our courses, explore the resources, and deepen your understanding of causal inference

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Share

Recommend content to colleagues, students, or anyone interested in causal methods

✏️

Contribute

Submit corrections, suggestions, or propose new content through our contact form

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Connect

Join discussions on causal inference topics and share your perspectives

License & Usage

Open Educational Resources

We believe in making educational materials as accessible as possible while protecting certain usage rights.

Educational Content License

All educational content on this site is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This allows sharing and adaptation for non-commercial purposes with proper attribution.

Code License

Code examples are provided under the MIT License, offering maximum flexibility for reuse in both open-source and commercial projects.

Contact

Get in Touch

We welcome questions, suggestions, collaboration inquiries, and feedback from the community.

Preferred Contact Methods

Contact Form: Use our contact form for general inquiries, content suggestions, or technical issues.

Email: For direct communication, email zephyr.v@outlook.com.

Response Time: We aim to respond to all inquiries within 3-5 business days.