When the treated units are spatial areas, their relationship with the control units is expected to exhibit a spatial relationship. Under the vertical regression framework, we propose a Bayesian approach for estimation of causal effects with spatial panel data.
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.
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.
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.
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.