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

Georgia Papadogeorgou, Fabrizia Mealli, Corwin M. Zigler

Research output: Contribution to journalArticle

Abstract

Interference arises when an individual's potential outcome depends on the individual treatment level, but also on the treatment level of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units within a cluster. However, within clusters there may be units that are more or less likely to receive treatment based on covariates or neighbors’ treatment. We define new estimands that describe average potential outcomes for realistic counterfactual treatment allocation programs, extending existing estimands to take into consideration the units’ covariates and dependence between units’ treatment assignment. We further propose entirely new estimands for population-level interventions over the collection of clusters, which correspond in the motivating setting to regulations at the federal (vs. cluster or regional) level. We discuss these estimands, propose unbiased estimators and derive asymptotic results as the number of clusters grows. For a small number of observed clusters, a bootstrap approach for confidence intervals is proposed. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.

Original languageEnglish (US)
Pages (from-to)778-787
Number of pages10
JournalBiometrics
Volume75
Issue number3
DOIs
StatePublished - Sep 1 2019

Fingerprint

Causal Inference
Ozone
Power plants
Pollution
Potential Outcomes
Power Plants
Unit
power plants
ozone
Population
confidence interval
pollution
Confidence Intervals
Technology
Interference
Covariates
Unbiased estimator
Power Plant
Number of Clusters
Bootstrap

Keywords

  • Interference
  • air pollution
  • inverse probability weighting
  • policy evaluation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Causal inference with interfering units for cluster and population level treatment allocation programs. / Papadogeorgou, Georgia; Mealli, Fabrizia; Zigler, Corwin M.

In: Biometrics, Vol. 75, No. 3, 01.09.2019, p. 778-787.

Research output: Contribution to journalArticle

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