Causal Markov condition for submodular information measures

Details Causal Markov condition for submodular information measures Causal Markov condition for submodular information measures

2010-02-22

arxiv.org/abs/1002.4020v1

Computer Science

Information Theory

21 pages, 4 figures

Scientific paper

The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a causal hypothesis in terms of a directed acyclic graph. In the conventional setting, the observations are random variables and the independence is a statistical one, i.e., the information content of observations is measured in terms of Shannon entropy. We formulate a generalized CMC for any kind of observations on which independence is defined via an arbitrary submodular information measure. Recently, this has been discussed for observations in terms of binary strings where information is understood in the sense of Kolmogorov complexity. Our approach enables us to find computable alternatives to Kolmogorov complexity, e.g., the length of a text after applying existing data compression schemes. We show that our CMC is justified if one restricts the attention to a class of causal mechanisms that is adapted to the respective information measure. Our justification is similar to deriving the statistical CMC from functional models of causality, where every variable is a deterministic function of its observed causes and an unobserved noise term. Our experiments on real data demonstrate the performance of compression based causal inference.

Affiliated with

Also associated with

No associations

Rating

Causal Markov condition for submodular information measures does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Causal Markov condition for submodular information measures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Causal Markov condition for submodular information measures will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-372804