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Dependent Happenings: A Recent Methodological Review.

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Summary
This summary is machine-generated.

Causal inference with interference examines how events and interventions affect individuals and populations. This framework helps understand direct and indirect effects, crucial for fields like public health and economics.

Keywords:
SUTVAcausal inferencecounterfactualdependent happeningsexperimental designherd immunityindirect effectsnetworkspeer influence effectspotential outcomespillover effects

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Area of Science:

  • Epidemiology
  • Social Sciences
  • Economics

Background:

  • Dependent happenings, where event frequency relies on affected individuals, were theorized by Sir Ronald Ross.
  • Interventions like vaccination offer direct benefits and indirect population-level (spillover) effects.
  • Causal inference provides a framework to define and estimate treatment effects from observed data.

Purpose of the Study:

  • To review definitions of effects for dependent happenings.
  • To explore causal inference with interference, where individual outcomes depend on others' treatments.
  • To discuss methods applicable across various disciplines.

Main Methods:

  • Review of causal inference frameworks.
  • Examination of interference models, including clustered and general forms.
  • Analysis of potential outcomes in dependent event scenarios.

Main Results:

  • Defined various effect types for dependent happenings.
  • Extended causal inference to scenarios with general interference.
  • Highlighted applicability across infectious diseases, social sciences, and economics.

Conclusions:

  • Causal inference with interference is essential for understanding complex population dynamics.
  • Methods for estimating effects with interference are crucial for policy and intervention design.
  • The reviewed methods offer broad applicability in scientific research.