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Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators

Isaac Gerber

arXiv 5 May 2026 · Statistics — Methodology

arXiv:2605.04124 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Modern heterogeneity-robust difference-in-differences estimators derive their asymptotic properties under iid, cluster, or fixed-design frameworks that abstract from complex survey sampling, yet practitioners routinely apply them to nationally representative surveys with stratified cluster designs. We show that, under standard regularity conditions, the influence functions of each smooth IF-based or regression-based modern DiD estimator satisfy Binder's (1983) smoothness conditions, so the standard stratified-cluster variance formula applied to their values produces design-consistent standard errors. A Monte Carlo study with 66,000 replications shows where the design effect comes from. HC1 standard errors that treat observations as iid produce coverage as low as 34% under a baseline survey design and below 11% under informative sampling. Combining the survey-weighted point estimate with PSU-level clustering - the practitioner's cluster=psu heuristic - recovers near-nominal coverage across all scenarios. Adding strata and finite-population corrections yields incremental precision but is not required for valid coverage. Survey-weighted doubly robust estimation produces well-calibrated inference when parallel trends hold only conditionally. An NHANES illustration of the ACA dependent coverage provision shows that point estimates and standard errors change substantively - enough to reverse significance conclusions - when the survey design is accounted for. We provide diff-diff (https://github.com/igerber/diff-diff), an open-source Python package implementing design-based variance for fifteen modern DiD estimators.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Binder, David A (1983) On the Variances of Asymptotically Normal Estimators from Complex Surveys1.00083100%
2Sant'Anna, Pedro H. C. and Zhao, Jun (2020) Doubly Robust Difference-in-Differences Estimators0.9416483%
3Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation0.9285480%
4Callaway, Brantly and Sant'Anna, Pedro H. C (2021) Difference-in-Differences with Multiple Time Periods0.92843100%
5de Chaisemartin, Clément and D'Haultfœuille, Xavier (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects0.8435460%
6Lumley, Thomas (2004) Analysis of Complex Survey Samples0.84333100%
7Sun, Liyang and Abraham, Sarah (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects0.84333100%
8Athey, Susan and Imbens, Guido W (2022) Design-Based Analysis in Difference-in-Differences Settings with Staggered Adoption0.73732100%
9Gerber, Isaac (2026) diff-diff: Difference-in-Differences Causal Inference for Python self0.64422100%
10Gardner, John (2022) Two-Stage Differences in Differences0.64422100%

Showing the top 10 of 29 scored citations.