Course Schedule

Topics

• Potential outcomes that depend on treatment sequences over time
• Staggered treatment adoptions and treatment entry/exit
• Causal parameters of interest
• Limited and no-carryover assumptions

Materials

Readings

• Roth, Sant'Anna, Bilinski & Poe (2023), "What's Trending in Difference-in-Differences?", Sections 2.1, 2.2, 2.3, and 3.1
• Robins (1986), "A New Approach To Causal Inference in Mortality Studies"
• Holland (1986), "Statistics and Causal Inference"

Topics

• Randomization at baseline versus sequential randomization
• Identification of average, distributional, and quantile treatment effects
• Efficient estimation with random staggered treatment allocations

Materials

Readings

• Bojinov, Rambachan & Shephard (2021), "Panel experiments and dynamic causal effects"
• Blackwell & Glynn (2018), "Causal Inferences with Time-Series Cross-Sectional Data"
• Roth & Sant'Anna (2023), "Efficient Estimation for Staggered Rollout Designs"
• Athey & Imbens (2022), "Design-based Analysis in DiD Settings"
• McKenzie (2012), "Beyond baseline and follow-up"

Topics

• Allowing for unobserved heterogeneity
• Parallel Trends and No-anticipation Assumptions
• Identification of ATT
• Estimation via influence functions and regressions
• Repeated cross-sections and unbalanced panels

Materials

Readings

• Roth et al. (2023), "What's Trending in Difference-in-Differences?", Section 2
• Baker et al. (2025), "Difference-in-Differences Designs: A Practitioner's Guide", Sections 1, 2, and 3
• Callaway (2023), "Difference-in-Differences for Policy Evaluation", Sections 1 and 2
• Angrist & Pischke (2009), "Mostly Harmless Econometrics", Section 5.2

Topics

• Limitations of TWFE linear specifications
• Regression adjustment, IPW, and doubly robust estimators
• Semiparametric efficiency
• Assessing overlap

Materials

Problem Set

Readings

• Roth et al. (2023), "What's Trending in Difference-in-Differences?", Sections 4.1 and 4.2
• Baker et al. (2025), "Difference-in-Differences Designs: A Practitioner's Guide", Section 4
• Heckman, Ichimura & Todd (1997), "Matching as an Econometric Evaluation Estimator"
• Abadie (2005), "Semiparametric DiD Estimators"
• Sant'Anna & Zhao (2020), "Doubly Robust DiD"
• Caetano & Callaway (2024), "Difference-in-Differences When Parallel Trends Holds Conditionally"
• Dias & Fontes (2024), "The Effects of a Large-Scale Mental Health Reform: Evidence from Brazil"
• Chernozhukov et al. (2018), "Double/Debiased Machine Learning"

Topics

• Clustering decisions
• Sampling vs. design uncertainty
• Inference with few clusters
• Parallel trends and functional form
• Selection and parallel trends

Materials

Readings

• Roth et al. (2023), "What's Trending in Difference-in-Differences?", Section 5
• Abadie et al. (2023), "When Should You Adjust Standard Errors for Clustering?"
• Bertrand, Duflo & Mullainathan (2004), "How Much Should We Trust DiD Estimates?"
• Roth & Sant'Anna (2023), "When Is PT Sensitive to Functional Form?"
• Ghanem, Sant'Anna & Wüthrich (2025), "Selection and parallel trends"

Topics

• Learning about treatment effect dynamics
• Different types of parallel trends
• Assessing plausibility of parallel trends
• Sensitivity analysis

Materials

Readings

• Baker et al. (2025), "Difference-in-Differences Designs: A Practitioner's Guide", Section 5.1
• Roth et al. (2023), "What's Trending in Difference-in-Differences?", Sections 4.3, 4.4, and 4.5
• Roth (2022), "Pretest with Caution"
• Callaway & Sant'Anna (2021), "Difference-in-Differences with Multiple Time Periods"
• Rambachan & Roth (2023), "A More Credible Approach to Parallel Trends"

Topics

• Causal parameters with staggered adoption
• Problems with standard TWFE specifications
• Heterogeneous-robust DiD estimators
• Semiparametric efficiency

Materials

Readings

• Baker et al. (2025), "Difference-in-Differences Designs: A Practitioner's Guide", Sections 5.2 and 5.3
• Roth et al. (2023), "What's Trending in Difference-in-Differences?", Section 3
• Callaway & Sant'Anna (2021), "Difference-in-Differences with Multiple Time Periods"
• Goodman-Bacon (2021), "Difference-in-Differences with Variation in Treatment Timing"
• Sun & Abraham (2021), "Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects"
• de Chaisemartin & D'Haultfœuille (2020), "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects"

Topics

• DiD with continuous and multi-valued treatments
• Triple differences designs

Materials

Problem Set

Readings

• Callaway, Goodman-Bacon & Sant'Anna (2024), "Difference-in-Differences with a Continuous Treatment"
• Ortiz-Villavicencio & Sant'Anna (2025), "Better Understanding Triple Differences"
• Olden & Møen (2022), "The triple difference estimator"
• Walker (2013), "Transitional Costs of Sectoral Reallocation"

Topics

• DiD when treatment can turn on and off
• Instrumented and fuzzy DiD

Materials

Readings

• de Chaisemartin & D'Haultfœuille (2020), "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects"
• de Chaisemartin & D'Haultfoeuille (2018), "Fuzzy Differences-in-Differences"
• de Chaisemartin & D'Haultfœuille (2024), "Difference-in-Differences Estimators of Intertemporal Treatment Effects"
• Miyaji (2024), "Instrumented Difference-in-Differences with Heterogeneous Treatment Effects"

Topics

• Causal inference with few units
• The classical synthetic control estimator
• Variations of the SC estimator
• Inference with synthetic controls

Materials

Readings

• Abadie (2021), "Using synthetic controls: Feasibility, data requirements, and methodological aspects"
• Abadie & Gardeazabal (2003), "Economic Costs of Conflict: Basque Country"
• Abadie, Diamond & Hainmueller (2010), "California's Tobacco Control Program"
• Chernozhukov, Wüthrich & Zhu (2021), "Conformal Inference Method"

Topics

• Synthetic controls with staggered designs
• Matrix completion
• Synthetic difference-in-differences
• Interactive fixed effects models
• SC and selection on unobservables

Materials

Readings

• Ben-Michael, Feller & Rothstein (2022), "Synthetic Controls with Staggered Adoption"
• Athey et al. (2021), "Matrix Completion Methods for Causal Panel Data Models"
• Arkhangelsky et al. (2021), "Synthetic Difference-in-Differences"
• Bai & Ng (2021), "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data"

Topics

• Identification based on lagged dependent variables
• Other fixed effects estimators
• Bridge functions
• Marginal structural models

Materials

Problem Set

Readings

• Athey & Imbens (2006), "Identification and Inference in Nonlinear Difference-in-Differences Models"
• Arkhangelsky & Imbens (2022), "Doubly Robust Identification for Causal Panel Data Models"
• Ding & Li (2019), "A Bracketing Relationship between Difference-in-Differences and Lagged-Dependent-Variable Adjustment"
• Viviano & Bradic (2024), "Dynamic Covariate Balancing: Estimating Treatment Effects over Time"

Topics

• Surrogate analysis for estimating long-term effects
• Combining experimental and observational data

Materials

Readings

• Athey, Chetty, Imbens & Kang (2025), "The Surrogate Index"
• Chen & Ritzwoller (2023), "Semiparametric estimation of long-term treatment effects"
• Athey, Chetty & Imbens (2020), "Combining experimental and observational data"
• Gray-Lobe, Pathak & Walters (2023), "Long-Term Effects of Universal Preschool in Boston"

Topics

• Student presentations of replication projects
• Discussion and feedback

Deliverable