Causal Inference as
Judea Pearl, University of California Los Angeles, USA
The traditional aim of machine learning methods is to infer
meaningful features of an underlying probability distribution
from samples drawn from that distribution.
With the help of such features, one can predict
relationships in yet unobserved samples, and infer
the likelihood of past and future events in light of new
evidence. Causal analysis goes one step further;
it aims at inferring features of the
data-generating process, that is, of the invariant strategy
by which Nature assigns values to the variables in the distribution.
These deeper features enable us to predict, not merely
relationships governed by the underlying distribution, but
also how that distribution would CHANGE when conditions are altered,
say, by deliberate interventions or by spontaneous
Although full description of the generating process cannot
be learned from data alone, many useful features of
the process can be learned from a combination of (1) data
(2) prior qualitative knowledge and/or (3) experiments.
Thus, the challenge
of causal inference is to answer practical queries about
the underlying process with minimum number of assumptions and with
I will present theoretical and methodological results concerning the
learnability of three types of queries:
(1) queries about the effect of potential interventions, (2) queries
about attribution (i.e., whether event x was
necessary for the occurrence of event y) and (3) queries
about the direct (or indirect) effect of one event on another.
For background information, see Causality (Pearl, 2000,
Cambridge University Press), or the following papers:
Judea Pearl is a professor of computer science and
statistics at the University of California, Los Angeles.
He joined the faculty of UCLA in 1970,
where he currently directs the Cognitive Systems Laboratory
and conducts research in artificial intelligence, human
reasoning and philosophy of science.
He has authored three books: Heuristics (1984),
Probabilistic Reasoning (1988), and Causality (2000).
A member of the National Academy of Engineering,
and a Founding Fellow the American
Association for Artificial Intelligence (AAAI),
Judea Pearl is the recipient of the IJCAI Research Excellence Award
for 1999, the London School of Economics Lakatos Award for 2001 and
the ACM Alan Newell Award for 2004.
In April 2008, he received the Benjamin Franklin Medal for
Computer and Cognitive Science from the Franklin Institute