Resources

This section contains a miscellaneous collection of lecture notes, videos, papers, workshops, etc. Please feel free to suggest additional references.

Table of contents

Papers

The 2026 JEL review, Difference-in-Differences Designs: A Practitioner’s Guide, is a strong starting point for the DiD framework. It organizes the literature around 2x2 building blocks, target parameters, weights, covariates, staggered adoption, and presents a “forward-engineering” approach.

The papers are listed in no particular order:

2026

Andrew Baker, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, Pedro Sant’Anna (2026). Difference-in-Differences Designs: A Practitioner’s Guide.

Satarupa Bhattacharjee, Bing Li, Lingzhou Xue (2026). A Test for Treatment Heterogeneity under a Distributional Difference-in-Difference Framework.

Brantly Callaway, Andrew Goodman-Bacon, Pedro H. C. Sant’Anna (2026). Difference-in-differences with a Continuous Treatment.

Yuhao Deng, Haoyu Wei, Zhongzhe Ouyang (2026). Difference-in-differences with a mediator.

Isaac Gerber (2026). Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators.

Jonas Skjold Raaschou-Pedersen (2026). Doubly Robust Instrumented Difference-in-Differences.

Hayato Tagawa (2026). Identification and Estimation of Staggered Difference-in-Differences with Network Spillovers.

Pedro H. C. Sant’Anna, Yifan Xu (2026). Difference-in-Differences with compositional changes. Journal of Econometrics.

2025

Brantly Callaway, Derek Dyal, Pedro H. C. Sant’Anna, Emmanuel S. Tsyawo (2025). Beyond Parallel Trends: An Identification-Strategy-Robust Approach to Causal Inference with Panel Data.

Clément de Chaisemartin, Bingxue Li, Diego Ciccia, Mélitine Malézieux, Doulo Sow, David Arboleda, Romain Angotti, Xavier d’Haultfoeuille, Felix Knau, Henri Fabre, Anzony Quispe Rojas (2025). Event-Study Estimators and Variance Estimators Computed by the did_multiplegt_dyn Command.

Chencheng Fang, Dominik Liebl (2025). Making Event Study Plots Honest: A Functional Data Approach to Causal Inference.

Michael Jetsupphasuk, Chenwei Fang, Didong Li, Michael G. Hudgens (2025). Difference-in-differences with stochastic policy shifts of a continuous treatment.

Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu (2025). Difference-in-Differences with Interval Data.

Fabrizia Mealli, Javier Viviens (2025). Difference-in-Differences in the Presence of Unknown Interference. SSRN version.

Arindrajit Dube, Daniele Girardi, Òscar Jordà, Alan M. Taylor (2025). A Local Projections Approach to Difference-in-Differences Event Studies. Journal of Applied Econometrics.

Hugo Faletto (2025). Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions. Working paper.

John Gardner, Neil Thakral, Linh Tô, Luther Yap (2025). Two-Stage Differences-in-Differences. Working paper.

Jeffrey M. Wooldridge (2025). Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators. Empirical Economics.

Dalia Ghanem, Pedro H. C. Sant’Anna, Kaspar Wüthrich (2025). Selection and Parallel Trends. Working paper.

2024

Jonathan Roth (2024). Interpreting Event-Studies from Recent Difference-in-Differences Methods.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2024). Difference-in-Differences Estimators of Intertemporal Treatment Effects. The Review of Economics and Statistics.

Carolina Caetano, Brantly Callaway (2024). Difference-in-Differences When Parallel Trends Holds Conditional on Covariates. Preprint, arXiv.

Nikolaj A. Harmon (2024). Difference-in-Differences and Efficient Estimation of Treatment Effects. Unpublished.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2024). Credible Answers to Hard Questions: Differences-in-Differences for Natural Experiments.

Dmitry Arkhangelsky, Guido W. Imbens (2024). Causal Models for Longitudinal and Panel Data: A Survey. The Econometrics Journal.

Kyle Butts (2024). Difference-in-Differences Estimation with Spatial Spillovers. Working paper.

Brantly Callaway, Andrew Goodman-Bacon, Pedro H. C. Sant’Anna (2024). Event-Studies with a Continuous Treatment. AEA Papers and Proceedings.

Clément de Chaisemartin, Xavier D’Haultfœuille (2024). Two-way Fixed Effects and Differences-in-Differences Estimators in Heterogeneous Adoption Designs. Working paper.

Kirill Borusyak, Xavier Jaravel, Jann Spiess (2024). Revisiting Event Study Designs: Robust and Efficient Estimation. The Review of Economic Studies.

2023

Andrew Gelman, Jessica Hullman, Lauren Kennedy (2023). Causal quartets: Different ways to attain the same average treatment effect

Ashesh Rambachan, Jonathan Roth (2023). A More Credible Approach to Parallel Trends. The Review of Economic Studies.

Clément de Chaisemartin, Xavier D’Haultfœuille (2023). Two-Way Fixed Effects and Differences-in-Differences Estimators with Several Treatments. Journal of Econometrics.

Clément de Chaisemartin, Xavier D’Haultfœuille (2023). Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey. The Econometrics Journal.

Jonathan Roth, Pedro H. C. Sant’Anna (2023). Efficient Estimation for Staggered Rollout Designs. Journal of Political Economy Microeconomics.

Kurt Schmidheiny, Sebastian Siegloch (2023). On Event Studies and Distributed-Lags in Two-Way Fixed Effects Models: Identification, Equivalence, and Generalization. Journal of Applied Econometrics.

2022

Yuya Sasaki, Takuya Ura (2022). Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias

P Rosenbaum, D Rubin (2022). Propensity scores in the design of observational studies for causal effects. Biometrika.

Jonathan Roth, Pedro H.C. Sant’Anna (2022). When Is Parallel Trends Sensitive to Functional Form?.

Andrew Baker, Jonah B. Gelbach (2022). Machine Learning and Predicted Returns for Event Studies in Securities Litigation.

Andrew C. Baker, David F. Larcker, Charles C. Y. Wang (2022). How Much Should We Trust Staggered Difference-In-Differences Estimates? Journal of Financial Economics.

Carolina Caetano, Brantly Callaway, Stroud Payne, Hugo Sant’Anna Rodrigues (2022). Difference in Differences with Time-Varying Covariates

Clément de Chaisemartin, Xavier d’Haultfoeuille, Félix Pasquier, Gonzalo Vazquez-Bare (2022). Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period.

Susanne Dandl, Torsten Hothorn, Heidi Seibold, Erik Sverdrup, Stefan Wager, Achim Zeileis (2022). What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?

Paul Goldsmith-Pinkham, Peter Hull, Michal Kolesár (2022). Contamination Bias in Linear Regressions

Pedro Picchetti, Cristine Pinto (2022). Marginal Treatment Effects in Difference-in-Differences.

Nandita Mitra, Jason Roy, Dylan Small (2022). The Future of Causal Inference. American Journal of Epidemiology.

Jonathan Roth, Pedro H.C. Sant’Anna, Alyssa Bilinski, John Poe (2022). What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.

Jonathan Roth (2022). Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends. American Economic Review: Insights.

Susan Athey, Guido W. Imbens (2022). Design-Based Analysis in Difference-In-Differences Settings with Staggered Adoption. Journal of Econometrics.

Liyang Sun, Jesse M. Shapiro (2022). Linear Panel Model with Heterogeneous Coefficients and Variation in Exposure. Journal of Economic Perspectives.

2021

Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido Imbens, Stefan Wager (2021). Synthetic Difference in Differences. American Economic Review.

Dmitry Arkhangelsky, Guido Imbens, Lihua Lei, Xiaoman Luo (2021). Double-Robust Two-Way-Fixed-Effects Regression For Panel Data.

Eli Ben-Michael, Avi Feller, Jesse Rothstein (2021). Synthetic Controls with Staggered Adoption.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2021). Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2021). Two-way fixed effects regressions with several treatments.

Xavier D’Haultfoeuille, Stefan Hoderlein, Yuya Sasaki (2021). Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments.

Bruno Ferman, Cristine Pinto (2021). Synthetic Controls With Imperfect Pretreatment Fit. Quantitative Economics.

John Gardner (2021). Two-stage differences in differences.

Andrew Goodman-Bacon (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics.

Pamela Jakiela (2021). Simple Diagnostics for Two-Way Fixed Effects

Kosuke Imai, In Song Kim, Erik Wang (2021). Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.

Kosuke Imai, In Song Kim (2021). On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data. Political Analysis.

Michelle Marcus, Pedro H. C. Sant’Anna (2021). The Role of Parallel Trends in Event Study Settings: An Application to Environmental Economics. Journal of the Association of Environmental and Resource Economists.

Jonathan Roth, Pedro H.C. Sant’Anna (2021). Efficient Estimation for Staggered Rollout Designs.

Simon Freyaldenhoven, Christian Hansen, Jorge Pérez Pérez, Jesse M. Shapiro (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper.

Michelle Marcus, Pedro H. C. Sant’Anna (2021). The Role of Parallel Trends in Event Study Settings: An Application to Environmental Economics. Journal of the Association of Environmental and Resource Economists.

2020

Brantly Callaway, Pedro H.C. Sant’Anna (2020). Difference-in-Differences with multiple time periods, Journal of Econometrics.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. American Economic Review.

Pedro H.C. Sant’Anna, Jun Zhao (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics.

Tymon Słoczyński (2020). Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights. The Review of Economics and Statistics.

Liyang Sun, Sarah Abraham (2020). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics.

2019 and earlier

Bruno Ferman, Cristine Pinto (2019). Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity. The Review of Economics and Statistics.

Simon Freyaldenhoven, Christian Hansen, Jesse M. Shapiro (2019). Pre-event Trends in the Panel Event-Study Design. American Economic Review.

Clément de Chaisemartin, Xavier D’Haultfoeuille (2018). Fuzzy differences-in-differences. The Review of Economic Studies.

Hans Fricke (2017). Identification based on difference-in-differences approaches with multiple treatments. Oxford Bulletin of Economics and Statistics.

Xavier D’Haultfoeuille, Stefan Hoderlein, Yuya Sasaki (2013). Nonlinear difference-indifferences in repeated cross sections with continuous treatments.

Susan Athey, Guido Imbens (2006). Identification and inference in nonlinear difference-indifferences models. Econometrica.

Mike Brewer, Thomas F. Crossley, Robert Joyce (2017). Inference with Difference-in-Differences Revisited. Journal of Econometric Methods.

Doruk Cengiz, Arindrajit Dube, Attila Lindner, Ben Zipperer (2019). The Effect of Minimum Wages on Low-Wage Jobs. Quarterly Journal of Economics.

Books

Here are some books and book-length resources that are useful for modern DiD work:

Clément de Chaisemartin, Xavier D’Haultfoeuille (2024). Credible Answers to Hard Questions: Differences-in-Differences for Natural Experiments. Working textbook under contract with Princeton University Press.

Martin Huber (2023). Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R.

Sylvain Chabé-Ferret (2025). Statistical Tools for Causal Inference.

Miguel Hernán, Jamie Robins (2022). Causal Inference: What If.

Nick Huntington-Klein (2021). The Effect.

Scott Cunningham (2020). Causal Inference: The Mix Tape.

Joshua D. Angrist, Jörn-Steffen Pischke (2014). Mastering ‘Metrics.

Matheus Facure (2023). Causal Inference for the Brave and True.

Blogs and notes

The blogs are sort of listed in terms of relevance. At least the first five entries can be considered as recommendations:

Scott Cunningham’s Scott’s Mixtape Substack is probably the best place on the internet for current discussions on DiD innovations.

Beatriz Gietner’s DiD Digest has some really amazing in depth entries on the mechanisms behind recent DiD innovations.

Pedro Sant’Anna has shared a comprehensive list of DiD resources on his personal website.

Jonathan Roth has shared a comprehensive list of DiD resources on his personal website.

Christine Cai has a working document which lists recent papers using different methods including DiDs.

Carlos Mendez has a post on DiD in R.

Matteo Courthoud. Medium blog on Causal inference

Kyle Butts. Personal blog with DiD entries.

Matheus Facure. Causal Inference for The Brave and True

Davis Schönholzer has a series of lectures on DiD here.

The World Bank’s Development Impact blog has several entries on DiD:

An Introduction to DiD with Multiple Time Periods by Brantly Callaway, Pedro H.C. Sant’Anna.

Jeffrey Wooldridge has several notes on DiD which are shared on his Dropbox including Stata dofiles.

Fernando Rios-Avila has a great explainer for the Callaway and Sant’Anna (2020) CS-DID logic on his blog.

Andrew C. Baker has notes on Difference-in-Differences Methodology with supporting material on GitHub.

Videos and online lectures

Jonas Peters has a four part lecture series on YouTube:

Yiqing Xu has a series of lecture on his YouTube channel.

Pedro H.C. Sant’Anna. Triple Differences Research Designs at Causal Solutions.

Brady Neal. A brief introduction to causal inference.

Nick Huntington-Klein has series of short videos on DiD literature on YouTube as part of The Effect book series.

Ben Elsner has a YouTube lecture series on causal inference including the new DiD literature.

Jorge Perez Perez has a YouTube lecture series with his co-authors (see xtevent in the Stata section for paper and package) on event studies with the following sequence of videos:

Josh Angrist (MIT) has an animated video on DiD here.

Paul Goldsmith-Pinkham has a brilliant set of slides on empirical methods including DiD on GitHub. These are also supplemented by his YouTube lecture series.

Scott Cunningham: CodeChella, one of the first DiD workshops, in July 2021. The recordings from the workshop are available at YouTube.

Taylor J. Wright organized an online DiD reading group in the summer of 2021. The lectures can be viewed on YouTube. Here is a playlist in the order they appear: