6 papers
Causal Structure Learning: a Bayesian approach based on random graphs
Mauricio Gonzalez-Soto, Ivan R. Feliciano-Avelino, L. Enrique Sucar +1
A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existe…
Causal Games and Causal Nash Equilibrium
Mauricio Gonzalez-Soto, Luis E. Sucar, Hugo J. Escalante
Classical results of Decision Theory, and its extension to a multi-agent setting: Game Theory, operate only at the associative level of information; this is, classical decision mak…
Reinforcement Learning is not a Causal problem
Mauricio Gonzalez-Soto, Felipe Orihuela Espina
We use an analogy between non-isomorphic mathematical structures defined over the same set and the algebras induced by associative and causal levels of information in order to argu…
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Mauricio Gonzalez Soto, David Danks, Hugo J. Escalante Balderas +1
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning. Decision-making has been well studied when the available information is cons…
A Guiding Principle for Causal Decision Problems
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We define a Causal Decision Problem as a Decision Problem where the available actions, the family of uncertain events and the set of outcomes are related through the variables of a…
Playing against Nature: causal discovery for decision making under uncertainty
M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
We consider decision problems under uncertainty where the options available to a decision maker and the resulting outcome are related through a causal mechanism which is unknown to…