Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periods
Not on the map. Why?
Abstract (arXiv)
This paper shows that the Conditional Quantile Treatment Effect on the Treated can be identified using a combination of (i) a conditional Distributional Difference in Differences assumption and (ii) an assumption on the conditional dependence between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. The second assumption recovers the unknown dependence from the observed dependence for the untreated group. We also consider estimation and inference in the case where all of the covariates are discrete. We propose a uniform inference procedure based on the exchangeable bootstrap and show its validity. We conclude the paper by estimating the effect of state-level changes in the minimum wage on the distribution of earnings for subgroups defined by race, gender, and education.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
- 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
- 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliancecited by 2
- 2025Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networkscited by 1
- 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance19 Sep 2025
- 2025Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks10 Jul 2025
- 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization6 Jun 2025
- 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction22 Jul 2024