 
Summary: Graphical models, causal inference, and
econometric models
Peter Spirtes
Abstract A graphical model is a graph that represents a set of conditional
independence relations among the vertices (random variables). The graph is often
given a causal interpretation as well. I describe how graphical causal models can
be used in an algorithm for constructing partial information about causal graphs
from observational data that is reliable in the large sample limit, even when some
of the variables in the causal graph are unmeasured. I also describe an algorithm
for estimating from observational data (in some cases) the total effect of a given
variable on a second variable, and theoretical insights into fundamental
limitations on the possibility of certain causal inferences by any algorithm
whatsoever, and regardless of sample size.
Keywords: graphical models, causal inference, model search, model testing
1 INTRODUCTION
A graphical model consists of a graph with vertices that are random
variables, and an associated set of joint probability distributions over the
random variables, all of which share a set of conditional independence
relations. The graph is often given a causal interpretation as well, in which
case it is a graphical causal model. Linear structural equation models with
