Causal Inference

Living notes by Causalica

Author

Amare Teklay

Published

February 12, 2026

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Causalica

Practical causal inference for real work

0.1 What this is

This is a living, opinionated set of notes on modern causal inference—written to be used, not merely read. It’s meant for researchers and practitioners who want to move from: question → design → identification → estimation → robustness → communication.

0.2 Who it’s for

  • Applied researchers who want a reliable workflow and mental models.
  • Economists, data scientists, policy analysts doing observational or experimental work.
  • Anyone who wants to build intuition for when a method works and why it fails.

0.3 How to use the book

  1. Start with Foundations to build shared language: potential outcomes, DAGs, identification.
  2. Jump to Methods when you have a concrete design in mind (DiD, IV, matching, synthetic control, etc.).
  3. Use Checklists & templates when writing: assumptions, threats, diagnostics, sensitivity.
Tip

If you’re reading this to solve a specific problem, begin with your estimand: What effect, on what population, under what intervention, compared to what alternative?

0.4 What you’ll get out of it

  • A clean way to translate real questions into estimands
  • Repeatable workflows for common designs
  • Practical guidance on assumption checks, diagnostics, and sensitivity analyses
  • Notes on pitfalls: post-treatment bias, bad controls, selection, interference, measurement issues

0.5 Roadmap

This book evolves. The near-term build plan:

  • Foundations: counterfactuals, DAGs, adjustment, SUTVA/interference, measurement
  • Core designs: randomized experiments, DiD, IV, RD, matching, synthetic controls
  • Modern tools: doubly robust estimation, causal ML (CATE/HTE), mediation (carefully), sensitivity
  • Practice: reporting templates, robustness checklists, “what can go wrong” library

0.6 Contributing and reuse

If you find an issue or want to contribute: - Suggest edits via the repo (issues/PRs), or share a short write-up and I’ll integrate it. - Contributions should favor clarity, assumption discipline, and reproducible examples.

Note

This is a living document. Expect iterative improvements and occasional restructuring.

0.7 About Causalica

Causalica is a small, focused effort to build: - trustworthy learning resources (this book), - practical tooling (checklists, templates, automation), - and “Amare-in-the-loop” workflows for applied causal analysis.

0.8 Cite this

  • Author: Amare Teklay
  • Title: Causal Inference: Living notes by Causalica
  • URL: textbook.causalica.com
  • Accessed: February 12, 2026

0.9 Contact

  • Email: amareteklay@gmail.com