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  "Description": "Methods for causal inference with spatio-temporal data.\nPoint process tools estimate causal effects over specified\nspatial and temporal windows from treatment and outcome events.\nUnit-level longitudinal tools estimate propensity scores and\nevaluate or learn stochastic treatment policies for discrete\nspatial or administrative units. See Papadogeorgou, et al.\n(2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024)\n<doi:10.31219/osf.io/5kc6f>.",
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      "concept": [
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      "title": "Plot simulated counterfactual densities",
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