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KDL has developed and released several open source software
packages as part of our research efforts:
Relational PC
Relational PC (RPC) is an implementation of the PC machine learning
algorithm1
designed for use with relational data.
RPC goes beyond learning statistical associations to
discover causal dependencies in relational data.
Given a database and schema, RPC outputs a partially directed
DAPER model that represents the equivalence class of
statistically indistinguishable causal models. The algorithm
retains the same essential strategies employed by PC for
identifying causal structure, but includes several key
innovations that enable learning in relational domains.
AIQ
AIQ 1.0 (automated identification of quasi-experiments) is a
proof-of-concept prototype system for automatically discovering
quasi-experiments for causal inference. AIQ identifies possible
quasi-experiments that can be performed on a specified data set with
the aim of finding causal relationships.
- Proximity
- Proximity is an open-source system for relational
knowledge discovery, incorporating major research findings from
the Knowledge Discovery Laboratory including model corrections
for statistical biases inherent in relational data such as
autocorrelation and degree disparity. Proximity implements
QGraph, a visual query language designed to support knowledge
discovery on large graph databases.
[1]. Spirtes, P., Glymour, C., and Scheines, R. (2000)
Causation, Prediction and Search, MIT Press.
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