We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset. While there are asymptotically correct...
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments a...
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
In this paper we examine the ability to perform causal reasoning with equilibrium models. We explicate a postulate, which we term the Manipulation Postulate, that is required in o...