fbargaglistoffi/NetworkCausalTree
Package for heterogeneous treatment and spillover effects under network interference
This package helps you understand how a treatment or intervention spreads through a network of connected individuals, like a social media campaign or a public health initiative. It takes your data on individuals, their connections, and their outcomes, then reveals how different groups are affected by the treatment and its ripple effects. Policy-makers, public health officials, and social scientists can use this to design more effective interventions.
Use this if you need to analyze the impact of an intervention where people are connected, and the effect on one person might influence others.
Not ideal if your intervention targets isolated individuals and there are no network interactions or spillover effects to consider.
Stars
9
Forks
5
Language
R
License
—
Category
Last pushed
Mar 16, 2026
Commits (30d)
0
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