arXiv 13 Jun 2025 · Econometrics
arXiv:2506.11960 · PDF · DOI · OpenAlex · Extracted main text
Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit.
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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 | robins1986new APACrefauthors Robins, J. APACrefauthors \ 1986 | 0.941 | 6 | 3 | 83% |
| 2 | hernan2020causal APACrefauthors Hernán, M. \ Robins, J. APACrefautho… (2020) 2020 | 0.928 | 5 | 3 | 80% |
| 3 | chernozhukov2018double APACrefauthors Chernozhukov, V. , Chetverikov… (2018) 2018 | 0.894 | 7 | 4 | 71% |
| 4 | bodory2022evaluating APACrefauthors Bodory, H. , Huber, M. \ Lafférs… (2022) 2022 | 0.874 | 21 | 7 | 67% |
| 5 | bradic2021high APACrefauthors Bradic, J. , Ji, W. \ Zhang, Y. APACre… (2024) 2024 | 0.843 | 10 | 6 | 60% |
| 6 | robins2000marginal APACrefauthors Robins, J. , Hernan, M A. \ Brumba… (2000) 2000 | 0.737 | 4 | 3 | 50% |
| 7 | robins2009estimation APACrefauthors Robins, J. \ Hernán, M. APACrefa… (2008) 2008 | 0.737 | 4 | 2 | 75% |
| 8 | lechner2009sequential APACrefauthors Lechner, M. APACrefauthors \ (2009) 2009 | 0.737 | 3 | 3 | 67% |
| 9 | murphy2003optimal APACrefauthors Murphy, S A. APACrefauthors \ (2003) 2003 | 0.737 | 3 | 3 | 67% |
| 10 | robins1994estimation APACrefauthors Robins, J. , Rotnitzky, A. \ Zha… (1994) 1994 | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 71 scored citations.