Laura Forastiere, Davide Del Prete, Valerio Leone Sciabolazza
arXiv 28 Apr 2020 · Statistics — Methodology · publishedSocial Networks (2023) · 5 citations (OpenAlex)
arXiv:2004.13459 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the case of interference, when a unit's treatment also affects other units' outcome. When interference is at work, policy evaluation mostly relies on the use of randomized experiments under cluster interference and binary treatment. Instead, we consider a non-experimental setting under continuous treatment and network interference. In particular, we define spillover effects by specifying the exposure to network treatment as a weighted average of the treatment received by units connected through physical, social or economic interactions. We provide a generalized propensity score-based estimator to estimate both direct and spillover effects of a continuous treatment. Our estimator also allows to consider asymmetric network connections characterized by heterogeneous intensities. To showcase this methodology, we investigate whether and how spillover effects shape the optimal level of policy interventions in agricultural markets. Our results show that, in this context, neglecting interference may underestimate the degree of policy effectiveness.
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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., Airoldi, E, & Mealli, F (2021) Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks self | 0.874 | 14 | 2 | 100% |
| 2 | Hirano, K., & Imbens, G. W (2004) The Propensity Score with Continuous Treatments | 0.874 | 6 | 3 | 67% |
| 3 | Magrini, E., P. Montalbano, S. Nenci, L. Salvatici (2016) Agricultural (Dis) Incentives and Food Security: Is there a Link? | 0.874 | 5 | 2 | 100% |
| 4 | Anderson, K., G. Rausser, and J. Swinnen (2013) Political Economy of Public Policies: Insights from Distortions to Agricultural and Food Markets | 0.811 | 4 | 2 | 100% |
| 5 | Ogburn, E.L., Sofrygin, O., Diaz, I. & van der Laan, M.J (2017) Causal inference for social network data | 0.737 | 3 | 2 | 100% |
| 6 | Sofrygin, O. & van der Laan, M (2017) Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Popu… | 0.737 | 3 | 2 | 100% |
| 7 | Van der Laan, M.J (2014) Causal Inference for a Population of Causally Connected Units | 0.737 | 3 | 2 | 100% |
| 8 | Anderson, K., S. Nelgen (2012) Updated National and Global Estimates of Distortions to Agricultural Incentives, 1955 to 2010 | 0.644 | 4 | 2 | 50% |
| Anderson | unmatched citation key Anderson | 0.644 | 4 | 1 | 100% |
| 10 | Aronow, P. M. & Samii, C (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 160 scored citations. 1 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.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach | 0.405 | 1 | 1 |
| 2 | Regression Discontinuity Design with Spillovers | 0.405 | 1 | 1 |
| 3 | Policy relevance of causal quantities in networks | 0.405 | 1 | 1 |