Yihong Liu, Gonzalo Vazquez-Bare
arXiv 13 Feb 2026 · Econometrics
arXiv:2602.13453 · PDF · DOI · OpenAlex · Extracted main text
When estimating treatment effects with two-way fixed effects (2WFE) models, researchers often use matching as a pre-processing step when the parallel trends assumption is thought to hold conditionally on covariates. Specifically, in a first step, each treated unit is matched to one or more untreated units based on observed time-invariant covariates. In the second step, treatment effects are estimated with a 2WFE regression in the matched sample, reweighting the untreated units by the number of times they are matched. We formally analyze this common practice and highlight two problems. First, when different treatment cohorts enter treatment in different time periods, the post-matching 2WFE estimator that pools all treated cohorts has an asymptotic bias, even when the treatment effect is constant across units and over time. Second, failing to account for the variability introduced by the matching procedure yields invalid standard error estimators, which can be biased upwards or downwards depending on the data generating process. We propose simple post-matching difference-in-differences estimators that compare each treated cohort to the never-treated separately, instead of pooling all treated cohorts. We provide conditions under which these estimators are consistent for well-defined causal parameters, and derive valid standard errors that account for the matching step. We illustrate our results with simulations and with an empirical application.
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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 | Abadie and Imbens (2016) Matching on the estimated propensity score | 0.928 | 5 | 4 | 80% |
| 2 | Abadie and Imbens (2006) Large sample properties of matching estimators for average treatment effects | 0.855 | 16 | 6 | 62% |
| 3 | Abadie and Spiess (2022) Robust post-matching inference | 0.843 | 5 | 4 | 60% |
| 4 | Lin, Ding, and Han (2023) Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect | 0.843 | 3 | 3 | 100% |
| 5 | Chen and Han (2024) On the limiting variance of matching estimators | 0.822 | 9 | 3 | 56% |
| 6 | Sun and Abraham (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.811 | 4 | 2 | 100% |
| 7 | Callaway and Sant' Anna (2021) Difference-in-differences with multiple time periods | 0.737 | 3 | 2 | 100% |
| 8 | LaLonde (1986) Evaluating the Econometric Evaluations of Training Programs with Experimental Data | 0.737 | 3 | 2 | 100% |
| 9 | Abadie and Imbens (2011) Bias-corrected matching estimators for average treatment effects | 0.644 | 2 | 2 | 100% |
| 10 | Abadie and Imbens (2012) A martingale representation for matching estimators | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 27 scored citations.