Philipp Bach, Sven Klaassen, Jannis Kueck, Mara Mattes, Martin Spindler
arXiv 10 Oct 2025 · Econometrics
arXiv:2510.09064 · PDF · DOI · OpenAlex · Extracted main text
Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, conditional on pre-treatment covariates. We introduce a novel approach to sensitivity analysis for DiD models that assesses the robustness of DiD estimates to violations of this assumption due to unobservable confounders, allowing researchers to transparently assess and communicate the credibility of their causal estimation results. Our method focuses on estimation by Double Machine Learning and extends previous work on sensitivity analysis based on Riesz Representation in cross-sectional settings. We establish asymptotic bounds for point estimates and confidence intervals in the canonical $2\times2$ setting and group-time causal parameters in settings with staggered treatment adoption. Our approach makes it possible to relate the formulation of parallel trends violation to empirical evidence from (1) pre-testing, (2) covariate benchmarking and (3) standard reporting statistics and visualizations. We provide extensive simulation experiments demonstrating the validity of our sensitivity approach and diagnostics and apply our approach to two empirical applications.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Brantly Callaway and Pedro HC Sant’Anna (2021) Difference-in-differences with multiple time periods | 1.000 | 13 | 4 | 100% |
| 2 | Ashesh Rambachan and Jonathan Roth (2023) A more credible approach to parallel trends | 1.000 | 12 | 4 | 100% |
| 3 | Carlos Cinelli and Chad Hazlett (2020) Making sense of sensitivity: Extending omitted variable bias | 1.000 | 10 | 5 | 100% |
| 4 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 6 | 3 | 100% |
| 5 | Robert J LaLonde (1986) Evaluating the econometric evaluations of training programs with experimental data | 1.000 | 6 | 3 | 100% |
| 6 | Jeffrey A. Smith and Petra E. Todd (2005) Does matching overcome lalonde's critique of nonexperimental estimators? | 0.959 | 17 | 4 | 88% |
| 7 | Pedro HC Sant’Anna and Jun Zhao (2020) Doubly robust difference-in-differences estimators | 0.946 | 26 | 6 | 85% |
| 8 | Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, and… (2024) Long story short: Omitted variable bias in causal machine learning, 2024 | 0.941 | 36 | 6 | 83% |
| 9 | Mirko Draca, Stephen Machin, and John Van Reenen (2011) Minimum wages and firm profitability | 0.843 | 15 | 4 | 60% |
| 10 | Neng-Chieh Chang (2020) Double/debiased machine learning for difference-in-differences models | 0.843 | 3 | 3 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.