Quantifying Causal Path-Specific Importance in Structural Causal Model

Quantifying Causal Influences

Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other n− 1 variables. However, quantifying the causal influence of one variable on another one remains a nontrivia...

Compound treatments, transportability, and the structural causal model: the power and simplicity of causal graphs.

Identifiability of Causal Effects in a Multi-Agent Causal Model

This paper introduces an algorithm that investigates whether the effect of an intervention is identifiable from a multi-agent causal model. A multi-agent causal model consists of a collection of agents each having access to a nondisjoint subset of the variables constituting the domain. Every agent has a causal model, determined by nonexperimental data and an acyclic causal diagram over its vari...

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...

The Causal Relationships among EDI Controls: A Structural Equation Model

Advances in EDI (Electronic Data Interchange) demand appropriate controls in order to realize the potential benefits from it. Formal, informal, and automated controls are basic parts of EDI controls. The state of one of three controls is suggested to affect performance indirectly through their effect on another controls in the research model. The causal relationships are tested using structural...