Date of Submission
2025
Date of Award
6-2025
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)
Supervisor
Garain, Utpal
Abstract (Summary of the Work)
Deep learning models often deliver high predictive accuracy; however, their lack of interpretability can hinder their adoption in critical fields such as healthcare and finance. This thesis explores the concept of Intrinsic Causal Contribution (ICC), a novel method for explaining neural network predictions by quantifying each input feature’s intrinsic causal influence on the output, independent of correlated effects. ICC models the network as a Structural Causal Model and employs Causal Normalizing Flows to handle complex dependencies, with efficient estimation via the Jansen Estimator. Analysis on both synthetic and real data sets provides evidence that ICC produces faithful, interpretable attributions, often outperforming traditional approaches like SHAP and LIME. By revealing truly influential features, ICC supports transparent and responsible AI, especially in sensitive settings such as medical diagnosis.
Control Number
CS2306
DOI
https://dspace.isical.ac.in/items/1a4f0dce-27be-47f8-9cf1-76aa3aad7b29
DSpace Identifier
http://hdl.handle.net/10263/7558
Recommended Citation
Rathore, Dhruv Vansraj, "Causal Explanations in Deep Learning Systems" (2025). Master’s Dissertations. 449.
https://digitalcommons.isical.ac.in/masters-dissertations/449