Attribution Projection Calculus: A Novel Framework for Causal Inference in Bayesian Networks

• URL: http://arxiv.org/abs/2505.12094v1
• Date: Sat, 17 May 2025 17:29:13 GMT
• Title: Attribution Projection Calculus: A Novel Framework for Causal Inference in Bayesian Networks
• Authors: M Ruhul Amin,
• Abstract summary: Projection Attribution Calculus (AP-Calculus) is a novel framework for determining causal relationships in structured Bayesian networks.<n>AP-Calculus provides a comprehensive mathematical foundation for analyzing feature-label attributions, managing spurious correlations, quantifying information gain, ensuring fairness, and evaluating uncertainty in prediction models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Attribution Projection Calculus (AP-Calculus), a novel mathematical framework for determining causal relationships in structured Bayesian networks. We investigate a specific network architecture with source nodes connected to destination nodes through intermediate nodes, where each input maps to a single label with maximum marginal probability. We prove that for each label, exactly one intermediate node acts as a deconfounder while others serve as confounders, enabling optimal attribution of features to their corresponding labels. The framework formalizes the dual nature of intermediate nodes as both confounders and deconfounders depending on the context, and establishes separation functions that maximize distinctions between intermediate representations. We demonstrate that the proposed network architecture is optimal for causal inference compared to alternative structures, including those based on Pearl's causal framework. AP-Calculus provides a comprehensive mathematical foundation for analyzing feature-label attributions, managing spurious correlations, quantifying information gain, ensuring fairness, and evaluating uncertainty in prediction models, including large language models. Theoretical verification shows that AP-Calculus not only extends but can also subsume traditional do-calculus for many practical applications, offering a more direct approach to causal inference in supervised learning contexts.

Related papers

• A Pre-training Framework for Relational Data with Information-theoretic Principles [57.93973948947743]
  We introduce Task Vector Estimation (TVE), a novel pre-training framework that constructs supervisory signals via set-based aggregation over relational graphs.<n>TVE consistently outperforms traditional pre-training baselines.<n>Our findings advocate for pre-training objectives that encode task heterogeneity and temporal structure as design principles for predictive modeling on relational databases.
  arXiv  Detail & Related papers  (2025-07-14T00:17:21Z)
• Generative Flow Networks: Theory and Applications to Structure Learning [7.6872614776094]
  This thesis studies the problem of structure learning from a Bayesian perspective.<n>It introduces Generative Flow Networks (GFlowNets)<n>GFlowNets treat generation as a sequential decision making problem.
  arXiv  Detail & Related papers  (2025-01-09T17:47:17Z)
• Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational Framework [3.69102525133732]
  "Space of networks" has a complex structure that cannot be adequately described using conventional statistical tools.<n>We introduce a measure-theoretic formalism for modeling generalized network structures such as graphs, hypergraphs, or graphs whose nodes come with a partition into categorical classes.<n>We show that our metric is an Alexandrov space of non-negative curvature, and leverage this structure to define gradients for certain functionals commonly arising in geometric data analysis tasks.
  arXiv  Detail & Related papers  (2024-09-10T07:58:37Z)
• On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions [49.8110352174327]
  We assess attribution methods from a perspective not previously explored in the graph domain: retraining. The core idea is to retrain the network on important (or not important) relationships as identified by the attributions. We run our analysis on four state-of-the-art GNN attribution methods and five synthetic and real-world graph classification datasets.
  arXiv  Detail & Related papers  (2024-01-01T02:03:35Z)
• KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification [75.95647590619929]
  Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. We propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics. A novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation.
  arXiv  Detail & Related papers  (2023-08-15T02:38:08Z)
• Efficient Computation of Counterfactual Bounds [44.4263314637532]
  We compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models. We evaluate their accuracy by providing credible intervals on the quality of the approximation.
  arXiv  Detail & Related papers  (2023-07-17T07:59:47Z)
• BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction [33.488229191263564]
  We propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view. It provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes.
  arXiv  Detail & Related papers  (2022-04-18T03:12:13Z)
• Neural Structured Prediction for Inductive Node Classification [29.908759584092167]
  This paper studies node classification in the inductive setting, aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. We present a new approach called the Structured Proxy Network (SPN), which combines the advantages of both worlds.
  arXiv  Detail & Related papers  (2022-04-15T15:50:27Z)
• Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding [22.123001954919893]
  We propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-12T11:27:46Z)
• CSNE: Conditional Signed Network Embedding [77.54225346953069]
  Signed networks encode positive and negative relations between entities such as friend/foe or trust/distrust. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. We introduce conditional signed network embedding (CSNE) Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail.
  arXiv  Detail & Related papers  (2020-05-19T19:14:52Z)
• Bidirectional Graph Reasoning Network for Panoptic Segmentation [126.06251745669107]
  We introduce a Bidirectional Graph Reasoning Network (BGRNet) to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes. BGRNet first constructs image-specific graphs in both instance and semantic segmentation branches that enable flexible reasoning at the proposal level and class level.
  arXiv  Detail & Related papers  (2020-04-14T02:32:10Z)
• Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies [83.05591911173332]
  junction tree algorithm is the most general solution for exact MAP inference with run-time guarantees. We propose a new graph transformation technique via node cloning which ensures a run-time for solving our target problem independently of the form of a corresponding clique tree.
  arXiv  Detail & Related papers  (2019-12-27T13:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.