Yechan Park, Yuya Sasaki
arXiv 18 Jun 2026 · Econometrics
arXiv:2606.20435 · PDF · DOI · OpenAlex · Extracted main text
Researchers using panel data to estimate causal effects routinely choose among three approaches to using past outcomes: difference-in-differences (DID), conditioning on lagged outcomes (matching, M), and a hybrid that does both (DIDM). The corresponding identifying assumptions are non-nested, leaving little guidance on which to report. We give conditions under which the corresponding estimands are ordered, with DIDM bracketed between matching and DID. This makes DIDM the minimax-regret choice among the three under a broad class of loss functions. We recommend reporting DIDM as the headline estimate, with matching and DID as bounds. We illustrate in 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 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers i: Evaluating bias in teacher value-added estimates | 1.000 | 5 | 3 | 100% |
| 2 | Dehejia, R. H. and Wahba, S (2002) Propensity score-matching methods for nonexperimental causal studies | 1.000 | 5 | 3 | 100% |
| 3 | LaLonde, R. J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.956 | 8 | 4 | 88% |
| 4 | Smith, J. A. and Todd, P. E (2005) Does matching overcome lalonde's critique of nonexperimental estimators? | 0.941 | 12 | 4 | 83% |
| 5 | Chabé-Ferret, S (2017) Should we combine difference in differences with conditioning on pre-treatment outcomes? | 0.928 | 5 | 3 | 80% |
| 6 | Dehejia, R. H. and Wahba, S (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.928 | 4 | 3 | 100% |
| 7 | Heckman, J. J., Ichimura, H., Smith, J. A., and Todd, P. E (1998) Characterizing selection bias using experimental data | 0.885 | 13 | 5 | 69% |
| 8 | Athey, S., Chetty, R., and Imbens, G (2025) The experimental selection correction estimator: Using experiments to remove biases in observational estimates | 0.874 | 6 | 3 | 67% |
| 9 | Heckman, J. J., Ichimura, H., and Todd, P (1998) Matching as an econometric evaluation estimator | 0.843 | 4 | 4 | 75% |
| 10 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers ii: Teacher value-added and student outcomes in adulthood | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 59 scored citations.