Search for: All records

In recent years the possibility of relaxing the so- called Faithfulness assumption in automated causal discovery has been investigated. The investiga- tion showed (1) that the Faithfulness assumption can be weakened in various ways that in an impor- tant sense preserve its power, and (2) that weak- ening of Faithfulness may help to speed up meth- ods based on Answer Set Programming. However, this line of work has so far only considered the dis- covery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting. 

Scientific models describe natural phenomena at different levels of abstraction. Abstract de- scriptions can provide the basis for interven- tions on the system and explanation of ob- served phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on work of Rubenstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model offers only an approx- imation of the underlying system. We show how the resulting account handles the discrep- ancy that can arise between low- and high- level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to prob- abilistic causal models, indicating how and where uncertainty can enter into an approxi- mate abstraction.