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Causal inference with interfering units for cluster and population level treatment allocation programs.

Georgia Papadogeorgou1, Fabrizia Mealli2, Corwin M Zigler3

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Summary
This summary is machine-generated.

This study introduces new methods for causal inference with interference, accounting for complex treatment assignments and population-level interventions. These advancements enable more accurate analysis of real-world scenarios, like environmental policy impacts.

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Area of Science:

  • Causal Inference
  • Statistics
  • Environmental Science

Background:

  • Interference in causal inference occurs when outcomes depend on others' treatments.
  • Partial interference assumes populations can be clustered, with outcomes only affected by within-cluster treatments.
  • Existing methods often assume random treatment allocation within clusters, which may not reflect reality.

Purpose of the Study:

  • To define new estimands for realistic counterfactual treatment allocation programs considering covariates and treatment dependence.
  • To propose novel estimands for population-level interventions across clusters.
  • To extend existing causal inference frameworks to handle complex interference scenarios.

Main Methods:

  • Defined new estimands for average potential outcomes under realistic, covariate-dependent treatment assignments.
  • Developed new estimands for cluster-level and population-level interventions.
  • Proposed unbiased estimators and derived asymptotic results for a growing number of clusters.
  • Suggested a bootstrap approach for confidence intervals with a small number of clusters.

Main Results:

  • The proposed estimands accurately capture average potential outcomes in scenarios with non-random treatment assignment within clusters.
  • New methods allow for the estimation of population-level intervention effects.
  • The study provides a framework for unbiased estimation and valid statistical inference.

Conclusions:

  • The developed methods extend causal inference to more realistic interference settings.
  • The new estimands and estimators are valuable for analyzing complex interventions and policies.
  • The approach was applied to a study on power plant emissions and ozone pollution, demonstrating practical utility.