carl-lib🔗ℹ

Moe Kayali

package: carl-lib

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.”

1 Language🔗ℹ

package: carl-lib

procedure
(create-inputs port) → inputs?
port : input-port?

Read a file in the CaRL language and output the set of rules and queries it contains.

package: carl-lib

procedure
(ground model conn) → (and/c graph? unweighted-graph?)
model : (listof rule?)
conn : connection?

Combine CaRL rules with a database instance to build a ground causal model.

package: carl-lib

procedure
(embed gcm) → matrix?
gcm : (listof pre-row?)

Apply embeddings to a semi-structured table to create flat table.

package: carl-lib

procedure
(detect model missing T Y) → (or/c list? #f)
  model : (listof rule?)
  missing : (listof predicate?)
  T : any/c
  Y : any/c

Return a minimal set of sufficient covariates for adjustment of T on Y. #f if no such set found. Set will not contain any elements from missing.

package: carl-lib

procedure
(construct augmented-gcm query Z) → (listof pre-row?)
  augmented-gcm : (and/c graph? unweighted-graph?)
  query : c-query?
  Z : list?

Construct a unit table given an augmented GCM and a set of covariates.

package: carl-lib

procedure
(estimate unit-table) → real?
unit-table : matrix?

Estimate the average treatment effect (ATE) given a unit table.