 
Summary: The TETRAD Project:
Constraint Based Aids to Causal Model Specification
Richard Scheines, Peter Spirtes, Clark Glymour,
Christopher Meek and Thomas Richardson
Department of Philosophy, Carnegie Mellon University 1
The statistical community has brought logical rigor and mathematical
precision to the problem of using data to make inferences about a model's
parameter values. The TETRAD project, and related work in computer
science and statistics, aims to apply those standards to the problem of
using data and background knowledge to make inferences about a model's
specification. We begin by drawing the analogy between parameter
estimation and model specification search. We then describe how the
specification of a structural equation model entails familiar constraints on
the covariance matrix for all admissible values of its parameters; we
survey results on the equivalence of structural equation models, and we
discuss search strategies for model specification. We end by presenting
several algorithms that are implemented in the TETRAD II program.
1. Motivation
A principal aim of many sciences is to model causal systems well enough to provide
sound insight into their structures and mechanisms, and to provide reliable predictions
