Causal Inference with Spatio-Temporal Data
Summarize results
Summarize results
Summarize results
Calculate the log counterfactual densities
Convert windows into sf objects
Get the baseline density
Generate a Hajek estimator for heterogeneity analysis
Get counterfactual densities
Get distance maps
Get distance maps from lines and polygons
Get the expectation of treatment events with arbitrary distances
Get elevation data
convert a list of im objects to a vector
Get causal estimates comparing two scenarios
Generate a Hajek estimator
Create a hyperframe
Obtain histories of treatment or outcome events
Generate observed densities
Get power densities
Plot estimated CATE
Calculate variance upper bounds
Generate average weighted surfaces
Generate a window
convert a list of im objects to a three-dimensional array
Get number of events in a pixel
Plot estimates
Plot estimates
Plot im
Plot observed densities
Plot weights
Perform out-of-sample prediction
Simulate counterfactual densities
Simulate power densities
Smooth outcome events
Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548>.