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Causal Inference on Networks under Continuous Treatment Interference

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  • Davide Del Prete
  • Laura Forastiere
  • Valerio Leone Sciabolazza

Abstract

This paper presents a methodology to draw causal inference in a non-experimental setting subject to network interference. Specifically, we develop a generalized propensity score-based estimator that allows us to estimate both direct and spillover effects of a continuous treatment, which spreads through weighted and directed edges of a network. 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 lead to a downward bias when assessing policy effectiveness.

Suggested Citation

  • Davide Del Prete & Laura Forastiere & Valerio Leone Sciabolazza, 2020. "Causal Inference on Networks under Continuous Treatment Interference," Papers 2004.13459, arXiv.org.
  • Handle: RePEc:arx:papers:2004.13459
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    2. Silvia Noirjean & Marco Mariani & Alessandra Mattei & Fabrizia Mealli, 2020. "Exploiting network information to disentangle spillover effects in a field experiment on teens' museum attendance," Papers 2011.11023, arXiv.org.

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