Estimating the effect of animal feeding operations on water quality in Iowa: a causal inference approach

Music City Center 

In many environmental settings, it is of primary interest to estimate the spillover (i.e., nonlocal) effect of a spatially varying intervention on its surrounding environment. For example, what is the impact of waste runoff from concentrated animal feeding operations (CAFOs) on water quality in Iowa? Estimating such an effect from observational data is challenging, due to (i) the risk of confounding bias, and (ii) the potential for treatment interference, namely, that multiple interventions affect the same outcome locations. To address this problem, we introduce a framework for causal inference with spatial data in which causal estimands are defined as functionals of the potential outcome distribution under a set of stochastic interventions. Corresponding nonparametric identifying assumptions are considered which allow the estimands to be estimated from observational data in the presence of arbitrary interference, and an augmented inverse probability of treatment-type estimator is proposed. We use the proposed method to estimate the average change in private well water quality that would be expected in settings with increasing and decreasing numbers of CAFOs

causal inference

spatial point process

interference

pollution

water quality

concentrated animal feeding operations (CAFOs) 

Section on Statistics and the Environment