Martin Huber, Eva-Maria Oeß
arXiv 25 Apr 2024 · Econometrics · publishedJournal of Applied Econometrics (2026)
arXiv:2404.16961 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces an overidentification test of two alternative assumptions to identify the average treatment effect on the treated in a two-period panel data setting: unconfoundedness and common trends. Under the unconfoundedness assumption, treatment assignment and post-treatment outcomes are independent, conditional on control variables and pre-treatment outcomes, which motivates including pre-treatment outcomes in the set of controls. Conversely, under the common trends assumption, the trend and the treatment assignment are independent, conditional on control variables. This motivates employing a Difference-in-Differences (DiD) approach by comparing the differences between pre- and post-treatment outcomes of the treatment and control group. Given the non-nested nature of these assumptions and their often ambiguous plausibility in empirical settings, we propose a joint test using a doubly robust statistic that can be combined with machine learning to control for observed confounders in a data-driven manner. We discuss various causal models that imply the satisfaction of either common trends, unconfoundedness, or both assumptions jointly, and we investigate the finite sample properties of our test through a simulation study. Additionally, we apply the proposed method to five empirical examples using publicly available datasets and find the test to reject the null hypothesis in two cases.
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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 | Chabé-Ferret, S (2017) Should We Combine Difference In Differences with Conditioning on Pre-Treatment Outcomes | 0.928 | 4 | 3 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.811 | 15 | 3 | 53% |
| 3 | Card, D. and A. B. Krueger (1994) Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania | 0.737 | 3 | 2 | 100% |
| 4 | Chang, N.-C (2020) Double/debiased machine learning for difference-in-differences models | 0.644 | 5 | 2 | 40% |
| 5 | Hernán, M. and J. Robins (2020) Causal Inference: What If | 0.644 | 2 | 2 | 100% |
| 6 | LaLonde, R (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.644 | 2 | 2 | 100% |
| 7 | Neyman, J (1959) Optimal asymptotic tests of composite statistical hypotheses | 0.644 | 2 | 2 | 100% |
| 8 | Robins, J. M., A. Rotnitzky, and L. Zhao (1994) Estimation of Regression Coefficients When Some Regressors Are not Always Observed | 0.644 | 2 | 2 | 100% |
| 9 | Sant'Anna, P. H. C. and J. B. Zhao (2020) Doubly Robust Difference-in-Differences Estimators | 0.644 | 2 | 2 | 100% |
| 10 | Abadie, A (2005) Semiparametric Difference-in-Differences Estimators | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation | 0.644 | 2 | 2 |
| 2 | Difference-in-Differences in the Presence of Unknown Interference | 0.405 | 1 | 1 |
| 3 | Causal Graphs for Conditional Parallel Trends | 0.405 | 1 | 1 |