Causal Inference with Spatio-Temporal Data
Convert windows into sf objects
Get the baseline density
Generate a Hajek estimator for heterogeneity analysis
Get counterfactual densities
Calculate the log 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
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 estimated CATE
Plot simulated counterfactual densities
Plot distance-based expectations
Plot estimates
Plot estimates
Plot im
Plot im objects (list)
Plot lists
Plot observed densities
Plot simulated power densities
Plot point pattern (list)
Plot weights
Perform out-of-sample prediction
Print results
Print results
Simulate counterfactual densities
Simulate power densities
Smooth outcome events
Summarize results
Summarize results
Summarize results
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> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.