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Causal inference in networks with continuous disorders

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  • Zhao Jun

    (Nankai University)

Abstract

In the case of perturbations when a treatment of one unit also affects the outcomes of other units, the SUTVA assumption of traditional causal inference is violated. When perturbations work, policy evaluation relies mainly on the assumptions of randomized experiments under cluster perturbations and binary treatments. Instead, we consider non-experimental treatments under continuous treatments and network perturbations. Speci

Suggested Citation

  • Zhao Jun, 2024. "Causal inference in networks with continuous disorders," Chinese Stata Conference 2024 15, Stata Users Group.
    • Handle: RePEc:boc:chin24:15
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    File URL: http://repec.org/chin2024/China24_Zhao.pdf
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