Causal Discovery with Prior Information
نویسندگان
چکیده
Bayesian networks (BNs) are rapidly becoming a leading tool in applied Artificial Intelligence (AI). BNs may be built by eliciting expert knowledge or learned via causal discovery programs. A hybrid approach is to incorporate prior information elicited from experts into the causal discovery process. We present several ways of using expert information as prior probabilities in the CaMML causal discovery program.
منابع مشابه
Causal Discovery via MML
Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental resul...
متن کاملUsing a New Tool to Visualize Environmental Data for Bayesian Network Modelling
This paper presents the software Omnigram Explorer, a visualization tool developed for interactive exploration of relations between variables in a complex system. Its objective is to help users gain an initial knowledge of their data and the relationships between variables. As an example, we apply it to the water reservoir data for Andalusia, Spain. Two Bayesian networks are learned using causa...
متن کاملCausal Induction 1 Running head : Continuous Causal Inference Causal Induction from Continuous Event Streams
Three experiments investigated the impact of delay on human causal learning. We present a new paradigm based on the presentation of continuous event streams, and use it to test two hypotheses drawn from associative learning theories of causal inference. Unlike free-operant procedures traditionally used to study temporal aspects of causal learning (Shanks, Pearson, & Dickinson, 1989; Shanks & Di...
متن کاملInvariant Gaussian Process Latent Variable Models and Application in Causal Discovery
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between the observed variables. Such models are then closely related to causal discovery in the presence of nonlinear confounders, which is a challenging problem. However, generally in such models the observation noise is assum...
متن کاملA Knowledge-Intensive Approach for Semi-automatic Causal Subgroup Discovery
This paper presents a methodological view on knowledge-intensive causal subgroup discovery implemented in a semi-automatic approach. We show how to identify causal relations between subgroups by generating an extended causal subgroup network utilizing background knowledge. Using the links within the network we can identify causal relations, but also relations that are potentially confounded and...
متن کامل