How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming

From probability to causality in probabilistic logic programming

Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system. When the structure of a probabilistic logic program is learned from data, however, only probabilistic information is used, and a single probabil...

Causal Reasoning with Bipartite Graphical Causal Models

This work proposes bipartite graphical causal models (BGCMs), in which the structure of a system of equations is encoded by a bipartite graph with variable and equation nodes, and forms a Markov property in terms of a new graphical separation criterion (B-separation) that exploits the functional determinism inherent in...

A Computationally Feasible Framework for Causal Probabilistic Explanation

Probabilistic Causal Impact (PCI) builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo.

Semantic Bayesian World Models

Semantic Bayesian World Models are envisioned: a Web that describes the world not as a database of facts but as a shared, evolving fabric of beliefs over knowledge graphs, where ontological axioms constrain priors, observations update beliefs by Bayesian conditioning, and actions intervene upon the world.

Reasoning about the past in epistemic logic with separation

A Unifying Perspective on Probabilities as Model Predictions

Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreover, it is unclear when and why acting on them actually leads to desirable outcomes. Here, we argue that every probability is the output of a...