arXiv 26 Dec 2020 · Econometrics
arXiv:2012.13710 · PDF · DOI · OpenAlex · Extracted main text
Randomized experiments have become a standard tool in economics. In analyzing randomized experiments, the traditional approach has been based on the Stable Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there is no interference between individuals. However, the SUTVA assumption fails to hold in many applications due to social interaction, general equilibrium, and/or externality effects. While much progress has been made in relaxing the SUTVA assumption, most of this literature has only considered a setting with perfect compliance to treatment assignment. In practice, however, noncompliance occurs frequently where the actual treatment receipt is different from the assignment to the treatment. In this paper, we study causal effects in randomized experiments with network interference and noncompliance. Spillovers are allowed to occur at both treatment choice stage and outcome realization stage. In particular, we explicitly model treatment choices of agents as a binary game of incomplete information where resulting equilibrium treatment choice probabilities affect outcomes of interest. Outcomes are further characterized by a random coefficient model to allow for general unobserved heterogeneity in the causal effects. After defining our causal parameters of interest, we propose a simple control function estimator and derive its asymptotic properties under large-network asymptotics. We apply our methods to the randomized subsidy program of \cite{dupas} where we find evidence of spillover effects on both short-run and long-run adoption of insecticide-treated bed nets. Finally, we illustrate the usefulness of our methods by analyzing the impact of counterfactual subsidy policies.
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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 | Pascaline Dupas (2014) Short-run subsidies and long-run adoption of new health products: Evidence from a field experiment | 1.000 | 10 | 5 | 100% |
| 2 | Michael G Hudgens and M. Elizabeth Halloran (2008) Toward causal inference with interference | 0.811 | 4 | 2 | 100% |
| 3 | Michael P. Leung (2020) Treatment and spillover effects under network interference | 0.811 | 4 | 2 | 100% |
| 4 | Gonzalo Vazquez-Bare (2020) Causal spillover effects using instrumental variables | 0.811 | 4 | 2 | 100% |
| 5 | Haiqing Xu (2018) Social interactions in large networks: A game theoretic approach | 0.737 | 3 | 3 | 67% |
| 6 | Guido W. Imbens and Joshua D. Angrist (1994) Identification and estimation of local average treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | James J. Heckman (1979) Sample selection bias as a specification error | 0.644 | 2 | 2 | 100% |
| 8 | Charles F. Manski (2012) Identification of treatment response with social interactions | 0.644 | 2 | 2 | 100% |
| 9 | Matthew O. Jackson, Zhongjian Lin, and Ning Neil Yu (2020) Adjusting for peer-influence in propensity scoring when estimating treatment effects, 2020 | 0.644 | 2 | 2 | 100% |
| 10 | D. B. Rubin (1990) on the application of probability theory to agricultural experiments. essay on principles. section 9 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Graph Neural Networks for Causal Inference Under Network Confounding | 0.405 | 1 | 1 |