Add to ORKGMany events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units of interest. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design-based standard errors that are straightforward to compute irrespective of spatial correlations in outcomes. Furthermore, I propose machine learning methods to find counterfactual candidate locations using ob...
In spatial econometrics,Wrefers to the matrix that weights the value of the spatially lagged variabl...
Political scientists often attempt to exploit natural experiments to estimate causal effects. We exp...
In epidemiologic studies, researchers are commonly interested in quantifying geospatial effects on t...
The scientific rigor and computational methods of causal inference have had great impacts on many di...
We consider design-based causal inference in settings where randomized treatments have effects that ...
Most spatial inquiries seek to investigate causal questions about spatial processes, but many quanti...
Spatial causal inference is an emerging field of research with wide ranging areas of applications. A...
abstract: A major challenge in health-related policy and program evaluation research is attributing ...
This paper demonstrates a method for estimating treatment effects in spatial tests, utilizing a seco...
In social sciences, data structures are often hierarchical. When these data also arise in spatial se...
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial stat...
Thesis (Ph.D.)--University of Washington, 2023Statistical machine learning techniques offer versatil...
Randomized controlled trials have become the gold standard for impact evaluation since they provide ...
BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisati...
Climate change has been identified as one the main public health challenges of this century and quan...
In spatial econometrics,Wrefers to the matrix that weights the value of the spatially lagged variabl...
Political scientists often attempt to exploit natural experiments to estimate causal effects. We exp...
In epidemiologic studies, researchers are commonly interested in quantifying geospatial effects on t...
The scientific rigor and computational methods of causal inference have had great impacts on many di...
We consider design-based causal inference in settings where randomized treatments have effects that ...
Most spatial inquiries seek to investigate causal questions about spatial processes, but many quanti...
Spatial causal inference is an emerging field of research with wide ranging areas of applications. A...
abstract: A major challenge in health-related policy and program evaluation research is attributing ...
This paper demonstrates a method for estimating treatment effects in spatial tests, utilizing a seco...
In social sciences, data structures are often hierarchical. When these data also arise in spatial se...
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial stat...
Thesis (Ph.D.)--University of Washington, 2023Statistical machine learning techniques offer versatil...
Randomized controlled trials have become the gold standard for impact evaluation since they provide ...
BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisati...
Climate change has been identified as one the main public health challenges of this century and quan...
In spatial econometrics,Wrefers to the matrix that weights the value of the spatially lagged variabl...
Political scientists often attempt to exploit natural experiments to estimate causal effects. We exp...
In epidemiologic studies, researchers are commonly interested in quantifying geospatial effects on t...