4.5k citations · 5.7k across the 23 of their papers we have counts for
14 papers · 1 filter
Accounting for hidden common causes when inferring cause and effect from observational data
David Heckerman
Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of con…
Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993)
David Heckerman, E. Mamdani
This is the Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, which was held in Washington, DC, July 9-11, 1993
Probabilistic Interpretations for MYCIN's Certainty Factors
David Heckerman
This paper examines the quantities used by MYCIN to reason with uncertainty, called certainty factors. It is shown that the original definition of certainty factors is inconsistent…
A Backwards View for Assessment
Ross D. Shachter, David Heckerman
Much artificial intelligence research focuses on the problem of deducing the validity of unobservable propositions or hypotheses from observable evidence.! Many of the knowledge re…
An Axiomatic Framework for Belief Updates
David Heckerman
In the 1940's, a physicist named Cox provided the first formal justification for the axioms of probability based on the subjective or Bayesian interpretation. He showed that if a m…
The Myth of Modularity in Rule-Based Systems
David Heckerman, Eric J. Horvitz
In this paper, we examine the concept of modularity, an often cited advantage of the ruled-based representation methodology. We argue that the notion of modularity consists of two…