The Causal Relational Learning (CaRL) framework is a tool for causal inference over relational data.
This software is currently in pre-alpha.
This is the reference implementation of the framework developed in the paper “Causal Relational Learning.”
Read a file in the CaRL language and output the set of
rules and queries it contains.
Combine CaRL rules with a database instance to build a ground causal model.
Apply embeddings to a ground causal model (GCM) to create the augmented GCM.
4 Covariate Detection
Return a minimal set of sufficient covariates for adjustment of T on Y.
#f if no such set found.
5 Unit Table Construction
Construct a unit table given an augmented GCM and a set of covariates.
Estimate the average treatment effect (ATE) given a unit table.