Source Themes

Addressing selection bias in cluster randomized experiments via weighting

In cluster randomized experiments with selection bias due to recruitment, data are often only available on those that were recruited. Based on a principal stratification framework, we show that causal effects on the overall population are identifiable based on the recruited sample only.

Spatial causal inference in the presence of unmeasured confounding and interference

We investigate the complications and opportunities when drawing causal inference from spatial observational data. We introduce causal diagrams that allow us to investigate the impact of spatial confounders, interference, and the inherent spatial structure in the exposure variable, and we illustrate that causal inference with spatial data has crucial differences to counterparts with independent observations. We then propose an approach that mitigates bias from unmeasured spatial confounding and incorporates interference within one framework.

Bayesian inference for aggregated Hawkes processes

Estimating the parameters of a temporal, spatio-temporal, or mutually-exciting Hawkes process based on data that are available in aggregated form by time, space, or both.

Bipartite causal inference with interference, time series data, and a random network

A framework for causal inference with bipartite interference from observational, time series data with a random bipartite network.

geocausal: An R package for spatio-temporal causal inference

Statistical software manuscript introducing the geocausal R package for spatio-temporal causal inference.

Covariate-informed latent interaction models: Addressing geographic & taxonomic bias in predicting bird-plant interactions

We propose a latent factor interaction model for networks measured with error, and a variable importance metric for latent models. We use the model to address the geographic and taxonomic bias of ecological studies of species' interactions, and identify the important bird and plant covariates for forming and detecting interactions.

Evaluating Federal Policies Using Bayesian Time Series Models: Estimating the Causal Impact of the Hospital Readmissions Reduction Program

Estimating the causal effect of the Hospital Readmissions Reduction on program on patient readmission and mortality rates.