arXiv 24 Mar 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2403.16177 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead. We establish two theoretical results. First, it is impossible to obtain informative bounds for the ATETS with no model restriction and no auxiliary data. Second, to overturn this negative result, we explore as a promising avenue the recent econometric developments in combining experimental and observational data (e.g., Athey et al., 2020, 2019); we indeed find that exploiting short-term experimental data can be informative without imposing classical model restrictions. Furthermore, building on Chesher and Rosen (2017), we explore how to systematically derive sharp identification bounds, exploiting both the novel data-combination principles and classical model restrictions. Applying the proposed method, we explore what can be learned about the long-run effects of job training programs on employment without long-term experimental data.
appendix boundary found by appendix_command · 61% of the source is main text. Read the extracted text to check this.
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 | |
|---|---|---|---|---|---|
| chernozhukov2019inference | unmatched citation key chernozhukov2019inference | 1.000 | 8 | 3 | 100% |
| ashenfelter1985susing | unmatched citation key ashenfelter1985susing | 1.000 | 7 | 3 | 100% |
| 3 | Athey, S., Chetty, R., and Imbens, G (2020) Combining experimental and observational data to estimate treatment effects on long term outcomes | 0.971 | 24 | 6 | 92% |
| imbens2015causal | unmatched citation key imbens2015causal | 0.928 | 5 | 4 | 80% |
| molchanov2018random | unmatched citation key molchanov2018random | 0.928 | 5 | 3 | 80% |
| 6 | Heckman, J. J (1981) Heterogeneity and state dependence | 0.928 | 4 | 3 | 100% |
| chernozhukov2014anti | unmatched citation key chernozhukov2014anti | 0.874 | 10 | 2 | 100% |
| 8 | Ghassami, A., Shpitser, I., and Tchetgen, E. T (2022) Combining experimental and observational data for identification of long-term causal effects | 0.874 | 9 | 2 | 100% |
| blundell2007changes | unmatched citation key blundell2007changes | 0.874 | 6 | 2 | 100% |
| 10 | Athey, S., Chetty, R., Imbens, G. W., and Kang, H (2019) The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 118 scored citations. 6 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.