Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
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Abstract (arXiv)
In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is is an unbiased estimator of a particular weighted average causal effect. We characterize the proeperties of this estimand, and show that the standard variance estimator is conservative.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Aug 2018.
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Citing papers per year: 2020: 1; 2021: 1; 2022: 0; 2023: 3; 2024: 3; 2025: 6; 2026: 1.
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- 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projectscited by 24
- 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Banditscited by 9
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- 2025Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Modelscited by 2
- 2020Causal Inference in Possibly Nonlinear Factor Modelscited by 1
- 2025Adaptively-weighted Nearest Neighbors for Matrix Completioncited by 1
- 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
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- 2023Causal Inference (C-inf) -- asymmetric scenario of typical phase transitionscited by 0
- 2024Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flowscited by 0
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
- 2026Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting9 May 2026
- 2025Counterfactual Forecasting for Panel Data9 Nov 2025
- 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects6 Nov 2025
- 2025N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion4 Jun 2025
- 2025Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models26 May 2025
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiD14 May 2025
- 2025Adaptively-weighted Nearest Neighbors for Matrix Completion14 May 2025
- 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning8 Dec 2024
- 2024Learning Counterfactual Distributions via Kernel Nearest Neighbors17 Oct 2024
- 2024Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows6 Sep 2024
- 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits9 Nov 2023
- 2023Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions2 Jan 2023
- 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Banditscited by 9
- 2024Learning Counterfactual Distributions via Kernel Nearest Neighborscited by 2
- 2025Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Modelscited by 2
- 2020Causal Inference in Possibly Nonlinear Factor Modelscited by 1
- 2025Adaptively-weighted Nearest Neighbors for Matrix Completioncited by 1
- 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
- 2023Causal Inference (C-inf) -- closed form worst case typical phase transitionscited by 0
- 2023Causal Inference (C-inf) -- asymmetric scenario of typical phase transitionscited by 0
- 2024Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flowscited by 0
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
- 2025N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completioncited by 0
- 2025Counterfactual Forecasting for Panel Datacited by 0
12 most cited of 13 All 13 (JSON)
- 2026Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting9 May 2026
- 2025Counterfactual Forecasting for Panel Data9 Nov 2025
- 2025N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion4 Jun 2025
- 2025Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models26 May 2025
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiD14 May 2025
- 2025Adaptively-weighted Nearest Neighbors for Matrix Completion14 May 2025
- 2024Learning Counterfactual Distributions via Kernel Nearest Neighbors17 Oct 2024
- 2024Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows6 Sep 2024
- 2023An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits9 Nov 2023
- 2023Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions2 Jan 2023
- 2023Causal Inference (C-inf) -- closed form worst case typical phase transitions2 Jan 2023
- 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projections1 Mar 2021
12 newest of 13 All 13 (JSON)