The Road Not Taken

by Robert Frost

Two roads diverged in a yellow wood,
And sorry I could not travel both
And be one traveler, long I stood
And looked down one as far as I could
To where it bent in the undergrowth;

Then took the other, as just as fair,
And having perhaps the better claim,
Because it was grassy and wanted wear;
Though as for that the passing there
Had worn them really about the same,

And both that morning equally lay
In leaves no step had trodden black.
Oh, I kept the first for another day!
Yet knowing how way leads on to way,
I doubted if I should ever come back.

I shall be telling this with a sigh
Somewhere ages and ages hence:
Two roads diverged in a wood, and I—
I took the one less traveled by,
And that has made all the difference.

A Personal Reflection: The Road Not Taken

This website is born from a personal journey—one that grapples with the very essence of "what if." After six years immersed in the study of international and development economics, pursuing a Ph.D. that promised a clear academic path, I made the difficult decision to step away. The road ahead seemed certain: publish, teach, contribute to the field. Yet another path called—one less defined, but brimming with different possibilities.

That choice, like Frost's traveler, was made with both hesitation and conviction. It left me wondering about the alternative reality—the one where I stayed. What discoveries might I have made? What contributions could I have offered? This curiosity about unseen outcomes, about the roads not taken, is at the heart of causal inference.

In research, as in life, we observe only one realized path. We see the treatment received, the policy implemented, the choice made. But what about the counterfactual? What would have happened under a different condition? Rubin's potential outcomes framework gives us the language to ask these questions rigorously. It reminds us that every observed outcome is just one realization among many possible worlds.

This project is my attempt to explore those other worlds—not just in my personal narrative, but in the scientific pursuit of understanding cause and effect. Through education, resources, and community, I hope to help others navigate their own diverging paths, whether in research, policy, or personal decision-making.

Educational Courses

Four levels of causal inference education, from fundamentals to advanced topics

Empirical Research Fundamentals

Beginner

Basic knowledge and skills about empirical research in social sciences, with a particular focus on quantitative research and relevant mathematical and/or statistical foundations

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Introduction to Causal Inference

Beginner/Intermediate

Introductory course to causal inference, including topics like potential outcomes model, directed acyclic graphs, basic research designs, and randomized control trials as the benchmark approach

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Intermediate Causal Inference

Intermediate

Typical, well-established, non-RCT methods for causal inference, such as subclassification, matching methods, difference-in-differences, panel fixed effects, instrumental variables, etc.

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Advanced Causal Inference

Advanced

More advanced topics and recent developments in causal inference, including machine learning, meta-learners, tree-based approaches, etc.

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