• Corpus ID: 220280274

Generalized propensity score approach to causal inference with spatial interference

  title={Generalized propensity score approach to causal inference with spatial interference},
  author={Andrew Giffin and Brian J. Reich and Shu Yang and Ana G. Rappold},
  journal={arXiv: Methodology},
Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under… 

Figures and Tables from this paper

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
These methods are extended to the spatiotemporal case where they compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses.
Instrumental variables, spatial confounding and interference
Unobserved spatial confounding variables are prevalent in environmental and ecological applications where the system under study is complex and the data are often observational. Instrumental
Rate-Optimal Cluster-Randomized Designs for Spatial Interference
We consider a potential outcomes model in which interference may be present between any two units but the extent of interference diminishes with spatial distance. The causal estimand is the global
Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions.
Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations
Bayesian Modeling for Exposure Response Curve via Gaussian Processes: Causal Effects of Exposure to Air Pollution on Health Outcomes
Motivated by environmental health research on air pollution, we address the challenge of estimation and uncertainty quantification of causal exposure-response function (CERF). The CERF describes the
Estimating intervention effects on infectious disease control: the effect of community mobility reduction on Coronavirus spread
Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. SusceptibleInfectious-Recovered (SIR)


Causal inference with interfering units for cluster and population level treatment allocation programs.
New estimands are defined 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.
Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes.
The proposed principal stratification method uses a spatial hierarchical model for potential pollution concentrations and ultimately uses estimates from this model to assess validity of assumptions regarding interference and applies it to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.
The central role of the propensity score in observational studies for causal effects
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.
Causal Inference Under Interference in Spatial Settings: A Case Study Evaluating Community Policing Program in Chicago
Abstract For decades, social scientists have been trying to answer causal questions about the effectiveness of certain programs or policies. The conventional methodology for answering such causal
A Bayesian view of doubly robust causal inference
In causal inference the effect of confounding may be controlled using regression adjustment in an outcome model, propensity score adjustment, inverse probability of treatment weighting or a
Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score
Building on new innovation in Bayesian computation, a technique for cutting feedback in a Bayesian propensity analysis is proposed, which severs feedback between the treatment and outcome giving propensity score estimates that are free from bias but modeled with uncertainty.
On Bayesian estimation of marginal structural models.
This article formalizes the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and shows that this leads to a natural Bayesian interpretation of IPT weighted estimation, and proposes the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting.
The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores
  • C. Zigler
  • Mathematics, Medicine
    The American statistician
  • 2016
This article explicates this fundamental feature of Bayesian estimation of causal effects with propensity scores to provide context for the existing literature and for future work on this important topic.
Toward Causal Inference With Interference
This article considers a population of groups of individuals where interference is possible between individuals within the same group, and proposes estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting.
Causal Inference in Infectious Diseases
The model proposed by Rubin for causal inference based on the potential outcomes if individuals received each of the treatments under study for infectious disease is reviewed, and the role of differential exposure to infection in direct and indirect effects is contrasted.