Causal independence for probability assessment and inference using Bayesian networks

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Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, background): “Many researches have been devoted to improving the performance of network construction [30], [32], [33] and probabilistic inference in causal graphs [34], [35].”
Building Probabilistic Networks: "Where Do the Numbers Come From?" Guest Editors Introduction (2000, methodology): “Examples of parametric probability distributions currently in use are modeled by the noisy-OR and noisy-AND gates and their generalizations [9], [ 15 ], [17], [29], [37].”
Learning Bayesian networks: approaches and issues (2011, background): “Other interesting papers on the subject include those by Heckerman and Breese (1996), Boutilier et al. (1996) and Zhang and Yan (1998).”
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach (2007, background): “Recall that a noisy-OR distribution is based on the assumption ofindependence of causal influence [2] and takes the form,”
Accuracy vs. efficiency trade-offs in probabilistic diagnosis (2002, background): “…reasonable to assume that each element on the probe’s path affects the probe’s outcome independently, so that there is no need to specify the probability of Ti for all possible value combinations of Xi1 , ..., Xik (the assumption known as causal independence (Pearl 1988; Heckerman & Breese 1995)).”