Probabilistic Structural Controllability in Causal Bayesian Networks
Humans routinely confront the following key question which could be viewed as a probabilistic variant of the controllability problem: While faced with an uncertain environment governed by causal structures, how should they practice their autonomy by intervening on driver variables, in order to increase (or decrease) the probability of attaining their desired (or undesired) state for some target variable? In this paper, for the first time, the problem of probabilistic controllability in Causal Bayesian Networks (CBNs) is studied. More specifically, the aim of this paper is two-fold: (i) to introduce and formalize the problem of probabilistic structural controllability in CBNs, and (ii) to identify a sufficient set of driver variables for the purpose of probabilistic structural controllability of a generic CBN. We also elaborate on the nature of minimality the identified set of driver variables satisfies. In this context, the term "structural" signifies the condition wherein solely the structure of the CBN is known.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models
In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also called structural causal models, is studied. Structural causal models are deterministic m…
Probabilistic Reasoning across the Causal Hierarchy
We propose a formalization of the three-tier causal hierarchy of association, intervention, and counterfactuals as a series of probabilistic logical languages. Our languages are of strictly increasing expressivity, the f…
Bayesian InferencecounterfactualAlgorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration. Unfortun…
counterfactualCausal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relati…
Bayesian Networks for Causal Analysis in Socioecological Systems
Causal and counterfactual reasoning are emerging directions in data science that allow us to reason about hypothetical scenarios. This is particularly useful in fields like environmental and ecological sciences, where in…
counterfactualCounterfactual Reasoning