Spillover Effects in the Presence of Unobserved Networks
Bibliographic Data
| ID | 6331982 |
|---|---|
| Authors | Naoki Egami (0000-0002-5491-2174, Columbia University, corresponding author) |
| Year | 2021 |
| Volume | 29 |
| Issue | 3 |
| Pages | 287-316 |
| Publication date | 2021-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/pan.2020.28 |
| OpenAlex | W3099385667 |
| Language | EN |
| Citations received | 3 |
| References cited | 45 |
When experimental subjects can interact with each other, the outcome of one individual may be affected by the treatment status of others. In many social science experiments, such spillover effects may occur through multiple networks, for example, through both online and offline face-to-face networks in a Twitter experiment. Thus, to understand how people use different networks, it is essential to estimate the spillover effect in each specific network separately. However, the unbiased estimation of thesenetwork-specific spillover effectsrequires an often-violated assumption that researchers observe all relevant networks. We show that, unlike conventional omitted variable bias, bias due to unobserved networks remains even when treatment assignment is randomized and when unobserved networks and a network of interest are independently generated. We then develop parametric and nonparametric sensitivity analysis methods, with which researchers can assess the potential influence of unobserved networks on causal findings. We illustrate the proposed methods with a simulation study based on a real-world Twitter network and an empirical application based on a network field experiment in China
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |