Econometrics & Causal Inference Resources
Published:
A [non-exhaustive] collection of resources for learning causal inference, econometrics, and data analysis.
Attribution: Many of these resources were compiled from Anna Stavniychuk’s Econometrics Library, an excellent repository that also includes extensive Russian-language materials for those interested. The detailed reading list on specific methods is available here.
Comprehensive Resource Lists
- Literature on Recent Advances in Applied Micro Methods (Christine Cai, 2025) — Part of her broader Public Goods page, which also collects links to PhD field course materials, coding guides (Stata, R, Python, LaTeX), and career advice from pre-grad school through tenure track [Carré dans l’axe — s/o Christine Cai].
- ‘Metrics Notes (Peter Hull) — Collection of lecture slides (undergraduate and graduate applied econometrics) [OG].
- Collection of lecture notes, videos, papers, workshops, etc. (Asjad Naqvi)
Causal Inference
Books
- Causal Inference: The Mixtape (Scott Cunningham, 2021)
- Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (Guido Imbens, Donald Rubin, 2015)
- A First Course in Causal Inference (Peng Ding, 2023)
- Causal Inference: What If (Miguel Hernan, Jamie Robins, 2020)
- Applied Causal Inference Powered by ML and AI (Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis, 2024)
- Introduction to Causal Inference (Brady Neal, 2020)
- The Effect: An Introduction to Research Design and Causality (Nick Huntington-Klein, 2022)
- Causal Inference for The Brave and True (Matheus Facure)
- Research Design in the Social Sciences (Declaration, Diagnosis, Redesign)
- Mostly Harmless Econometrics (Joshua D. Angrist, Jörn-Steffen Pischke)
- Mastering ‘Metrics (Joshua D. Angrist, Jörn-Steffen Pischke)
- Statistical Tools for Causal Inference (Sylvain Chabé-Ferret, 2022)
- Counterfactuals and Causal Inference: Methods and Principles for Social Research (Stephen L. Morgan, Christopher Winship, 2007)
- The Theory and Practice of Field Experiments: An Introduction from the EGAP Learning Days
- Using R for Introductory Econometrics (Heiss, 2016)
- Causality (Judea Pearl, 2009)
- Causal Inference in Statistics (Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016)
- The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie, 2018)
- A Guide on Data Analysis
- Marketing Research
Video Lectures
- The Effect: Econometrics, Causality, and Coding with Dr. HK (Nick Huntington-Klein)
- Econometrics, Causality, and Coding with Dr. HK (Nick Huntington-Klein)
- Causal Inference – Online Lectures, M.Sc/PhD Level (Ben Elsner)
- Graduate Econometrics Course (Ben Lambert) [Carré dans l’axe ++]
- Visualization, Identification, and Estimation in the Linear Panel Event-Study Design (Jesse Shapiro, Christian Hansen)
- Applied Methods PhD Course (Paul Goldsmith-Pinkham, 2021)
- DiD Reading Group (Taylor Wright) and Other DiD Seminars
- Introduction to Econometrics (Ivan A. Canay, 2021)
- Topics in Econometric Theory (Ivan A. Canay, 2021)
- Doubly Robust Estimation of Treatment Effects with Machine Learning Methods
- NBER SI 2024 Methods Lecture: New Developments in Experimental Design and Analysis
- Analysis and Design of Multi-Armed Bandit Experiments and Policy Learning (Susan Athey)
- Interference and Spillovers in Randomized Experiments (Guido Imbens)
AEA Continuing Education Webcasts
- Mastering Mostly Harmless Econometrics (Alberto Abadie, Joshua Angrist, and Christopher Walters, 2020)
- Cross-Section Econometrics (Alberto Abadie, Joshua Angrist, Christopher Walters, 2017)
- Time Series Econometrics (James H. Stock and Mark W. Watson, 2015)
- Cross-Section Econometrics (Alberto Abadie and Joshua Angrist, 2014)
- Time Series Econometrics (Giorgio Primiceri and Frank Schorfheide, 2013)
- Cross-Section Econometrics (Guido Imbens and Jeffrey Wooldridge, 2012)
- Time-Series Econometrics (James H. Stock and Mark W. Watson, 2010)
- Cross-Section Econometrics (Jeffrey Wooldridge and Guido Imbens, 2009)
Course Materials
- Mixtape-Sessions (Scott Cunningham)
- ARE213 PhD Applied Econometrics (Kirill Borusyak, UC Berkeley)
- Introduction to Causal Inference (Brady Neal, 2020)
- Causal Inference with Applications (Kosuke Imai, 2021)
- Causal Inference with Applications (Matthew Blackwell, 2021)
- Causal Inference for the Social Sciences (Jasjeet S. Sekhon, 2015)
- Program Evaluation for Public Service (Andrew Heiss, 2020)
- Class Material in Statistics and Econometrics (Paolo Zacchia)
- Introduction to Statistics with Computer Applications (Kyle F Butts)
- Applied Empirical Methods (Paul Goldsmith-Pinkham)
- Applied Econometrics at NYU Stern (Chris Conlon)
- Data Science for Business Applications (Magdalena Bennett)
- Probability and Statistics / Econometric Theory / Microeconometrics (Paolo Zacchia)
- Graduate Econometrics (Ivan A. Canay)
- Topics in Econometrics (Ivan A. Canay)
- Causal Inference (Stefan Wager)
- Econometrics – Undergraduate (Daniele Girardi)
- Introduction to Probability and Statistics (Kyle Butts)
Lecture Notes & Papers
- A Refresher on (Matrix Notation) Basic OLS Properties (NYU)
- A Crash Course in Good and Bad Controls (Judea Pearl)
- Weak IV – Lecture Notes (Jörn-Steffen Pischke, LSE)
Blogs
- Causal AI Blog (Brady Neal)
- Causal Analysis in Theory and Practice
- Nick Huntington-Klein
- Christine Cai
- Andrew Baker
- Paul Goldsmith-Pinkham
- David Schönholzer
Twitter / X
EconTwitter (Mastodon)
General Econometrics
Lecture Notes & Courses
- Lecture Notes in Microeconometrics (Kurt Schmidheiny) [Carré dans l’axe ++] — Concise, self-contained short guides covering core microeconometric topics. Excellent quick-reference material.
- Graduate Econometrics Lecture Notes (Matteo Courthoud) — Comprehensive notes originally from the PhD Econometrics course of Damian Kozbur (Zurich).
- Machine Learning for Economics (Matteo Courthoud) — Notes from Damian Kozbur’s ML for Economic Analysis course (Zurich). Python/Jupyter-based exercises drawing from An Introduction to Statistical Learning and Elements of Statistical Learning.
- Econometrics and Statistics (Jean-Paul Renne) — Covers linear regression, discrete-choice models, and introduction to time series analysis, with R-based examples and simulations.
- Econometricks: Short Guides to Econometrics (Davud Rostam-Afschar, Universität Mannheim) — Concise guides with tricks and tips covering probability theory, specific distributions, the least squares estimator, Frisch-Waugh-Lovell, MLE, and GMM.
Cheat Sheets & Quick References
- The Econometrics Cheat Sheet Project (Marcelo Moreno Porras) — LaTeX-typeset cheat sheets covering: basic econometrics concepts (OLS, hypothesis testing, dummy variables, structural change); time series (stationarity, cointegration, autocorrelation); and additional topics (IV/TSLS, matrix notation, VAR/SVAR/VECM, logistic regression).
Mathematics & Linear Algebra Refreshers
- Essence of Linear Algebra (3Blue1Brown)
- Introduction to Probability, Statistics, and Random Processes (Hossein Pishro-Nik)
- A Refresher on (Matrix Notation) Basic OLS Properties (NYU)
- ProofWiki
Python for Data Analysis & Econometrics
- Econometrics With Python (Weijie Chen, 2022)
- Causal Inference for The Brave and True (Matheus Facure) — Also listed under Causal Inference above.
R for Data Analysis
Books
- R for Data Science (Garrett Grolemund, Hadley Wickham, 2023)
- R Cookbook (Paul Teetor, 2019)
- Advanced R (Hadley Wickham, 2019)
- Advanced R Solutions (Malte Grosser, Hadley Wickham, 2019)
- R Packages (Hadley Wickham, Jennifer Bryan, 2023)
- An Introduction to R: Notes on R – A Programming Environment for Data Analysis and Graphics (W. N. Venables, D. M. Smith, 2022)
- Using R for Introductory Econometrics (Florian Heiss, 2020)
- Guide to R for SCU Economics Students (William A. Sundstrom, Michael J. Kevane)
- Introduction to Econometrics with R (Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer)
- R for Data Science (Hadley Wickham, Garrett Grolemund)
- Hands-On Programming with R (Garrett Grolemund)
- Data Science: A First Introduction (Tiffany Timbers, Trevor Campbell, and Melissa Lee)
- YaRrr! The Pirate’s Guide to R (Nathaniel D. Phillips)
R Notebooks
- Introduction to R (Hans H. Sievertsen)
- Applied Economics with R (Hans H. Sievertsen)
- Data Science for Economists (Grant R. McDermott)
- Library of Statistical Techniques (Nick Huntington-Klein et al.)
Resource Collections
- List of Open Source Books about R (Pere A. Taberner)
- Big Book of R (Oscar Baruffa)
- Data Science Course in a Box
- Enhance and Advance
LaTeX & Academic Writing Tools
Tools for creating figures, tables, and documents for academic papers.
Note-Taking & Knowledge Management
- Obsidian — The “everything app” for researchers. Markdown-based, local-first note-taking with bidirectional linking, graph view, and a massive plugin ecosystem. Excellent for building a personal knowledge base (Zettelkasten), literature reviews, and connecting ideas across papers. Integrates well with Zotero via plugins. Free for personal use.
Open-source alternatives:
Logseq — Open-source, outliner-based knowledge management. Block-level linking and queries, built-in PDF annotation, and local-first storage. Strong for daily journaling workflows and task management alongside notes.
Zettlr — Open-source markdown editor designed specifically for academic writing. Built-in citation support (BibTeX/CSL-JSON), Zettelkasten features, and export to multiple formats. Lighter weight than Obsidian.
Joplin — Open-source note-taking with end-to-end encryption and sync across devices. Markdown support, tagging, and notebook organization. Good if privacy and cross-platform sync are priorities.
Foam — Open-source, VS Code-based personal knowledge management. If you already live in VS Code for coding, Foam brings Obsidian-like linking and graph visualization to your existing workflow.
Figure & Diagram Creation
TikZiT — Lightweight graphical editor for creating node-and-edge diagrams (graphs, string diagrams, game trees). Outputs clean, minimal TikZ code that integrates directly into LaTeX documents. Particularly useful for theoretical diagrams in economics.
Mathcha — Online visual equation and diagram editor with TikZ export. Intuitive interface for flowcharts, decision trees, and geometric figures. Good for quick prototyping before refining in LaTeX.
DAGitty — Browser-based environment for creating, editing, and analyzing causal diagrams (DAGs). Beyond drawing, it identifies adjustment sets, instrumental variables, and testable implications. Essential for applied causal inference work. Also available as an R package and pairs well with ggdag for ggplot2-style visualization.
Writing & Collaboration
- Overleaf — Online LaTeX editor with real-time collaboration, version history, and templates. Standard tool for co-authored papers. Integrates with Git for local editing.
Live Preview Editors
TeXstudio — Full-featured LaTeX IDE with integrated PDF viewer, auto-completion, and syntax highlighting. Good for local editing.
ktikz / qtikz — Live-preview TikZ editors. Not drag-and-drop, but instant visual feedback makes iterating on figures much faster.
Last updated: February 2026