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

TL;DR

This paper develops new causal inference methods for interference settings with cluster and population-level treatments, accounting for covariates and treatment dependence, and applies these methods to environmental policy evaluation.

Contribution

It introduces novel estimands and unbiased estimators for realistic treatment allocation programs considering covariates and dependence, extending existing causal inference frameworks.

Findings

Proposed estimators are unbiased and asymptotically normal.

Applied methods to assess power plant emission reduction effects.

Extended causal inference to population-level interventions.

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. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.