Silvia Noirjean, Marco Mariani, Alessandra Mattei, Fabrizia Mealli
arXiv 22 Nov 2020 · Statistics — Applications · publishedJournal of Educational and Behavioral Statistics (2024) · 1 citations (OpenAlex)
arXiv:2011.11023 · PDF · DOI · OpenAlex · Extracted main text
A key element in the education of youths is their sensitization to historical and artistic heritage. We analyze a field experiment conducted in Florence (Italy) to assess how appropriate incentives assigned to high-school classes may induce teens to visit museums in their free time. Non-compliance and spillover effects make the impact evaluation of this clustered encouragement design challenging. We propose to blend principal stratification and causal mediation, by defining sub-populations of units according to their compliance behavior and using the information on their friendship networks as mediator. We formally define principal natural direct and indirect effects and principal controlled direct and spillover effects, and use them to disentangle spillovers from other causal channels. We adopt a Bayesian approach for inference.
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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 | Forastiere, L., Lattarulo, P., Mariani, M., Mealli, F. Razzolini, L (2019) Exploring encouragement, treatment and spillover effects using principal stratification, with application to a field experiment… self | 1.000 | 14 | 5 | 100% |
| 2 | Lattarulo, P., Mariani, M. Razzolini, L (2017) Nudging museums attendance: A field experiment with high school teens self | 1.000 | 5 | 3 | 100% |
| 3 | Forastiere, L., Mealli, F. VanderWeele, T. J (2016) Identification and estimation of causal mechanisms in clustered encouragement designs: Disentangling bed nets using Bayesian pri… self | 0.874 | 5 | 2 | 100% |
| 4 | Frangakis, C. E. Rubin, D. B (2002) Principal stratification in causal inference | 0.874 | 5 | 2 | 100% |
| 5 | Frangakis, C. E., Rubin, D. B. Zhou, X. H (2002) Clustered encouragement design with individual noncompliance: Bayesian inference and application to Advance Directive Forms (wit… | 0.737 | 3 | 2 | 100% |
| 6 | Forastiere, L., Airoldi, E. M. Mealli, F (2021) Identification and estimation of treatment and interference effects in observational studies on networks | 0.644 | 2 | 2 | 100% |
| 7 | Hirano, K., Imbens, G. W., Rubin, D. B. Zhou, X. H (2000) Assessing the effect of an influenza vaccine in an encouragement design | 0.644 | 2 | 2 | 100% |
| 8 | Imbens, G. W. Rubin, D. B (1997) Bayesian inference for causal effects in randomized experiments with noncompliance | 0.644 | 2 | 2 | 100% |
| 9 | Pearl, J (2001) Direct and indirect effects | 0.644 | 2 | 2 | 100% |
| 10 | Robins, J. M. Greenland, S (1992) Identifiability and exchangeability for direct and indirect effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating the Causal Effect of an Intervention in a Time Series Setting: the C-ARIMA Approach | 0.405 | 1 | 1 |