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. …
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 …
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 …
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 …
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.
Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq
Evaluating the effect of a point pattern treatment on a point pattern outcome measured over time.
Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties
We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior …
Soft tensor regression
Estimation of tensor regression coefficients on a soft version of the PARAFAC approximation.
Propensity Score Weighting for Causal Subgroup Analysis
Causal inference for subgroup analysis and the Connect-S plot.
Bipartite Causal Inference with Interference
Extending causal inference in the presence of interference to bipartite settings where the interventional units are different from the …
Mitigating Unobserved spatial confounding when estimating the effect of supermarket access on cardiovascular disease deaths
Addressing confounding bias from unmeasured spatial variables using mixed models.
A Causal Exposure Response Function with Local Adjustment for Confounding: A study of the health effects of long-term exposure to low levels of fine particulate matter
Causal exposure-response curve estimation with local confonding adjustment. Different variables confound the exposure-response …
Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions
A review of the literature on the health effects of exposure to low levels of particulate matter.
