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Information-Theoretic Causal Inference and Discovery

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2758

Special Issue Editor


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Guest Editor
ECE Department, Purdue University, West Lafayette, IN 47907, USA
Interests: causal inference; causal discovery; information theory

Special Issue Information

Dear Colleagues,

Causal inference is one of the main focus areas in artificial intelligence (AI) and machine learning (ML). Causality has received significant interest in ML in the recent years in part due to its utility for generalization and robustness. It is also central for tackling decision-making problems such as bandit problems, reinforcement learning, policy, or experimental design. Information-theoretic assumptions and techniques open new avenues for causality research ranging from discovery to inference. Some examples of the success of information theory in causal inference are the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery, the use of the information bottleneck principle with applications in the generalization of machine learning models, and analyzing causal structures of deep neural networks with information theory, among others.

This Special Issue focuses on bringing information theory and causality together to expand the scope of current causal reasoning algorithms. The expected contributions range from the introduction of new assumptions to pave the way for better analyses to addressing a causal question in a well-studied setting using a novel information-theoretic approach. Some applications include causal graph discovery and the identification of interventional or counterfactual distributions from data.

Dr. Murat Kocaoglu
Guest Editor

Manuscript Submission Information

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Keywords

  • causal graphs
  • causal discovery
  • causal inference
  • experimental design
  • information theory
  • entropy

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Published Papers (1 paper)

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Research

24 pages, 1935 KiB  
Article
Comparing Causal Bayesian Networks Estimated from Data
by Sisi Ma and Roshan Tourani
Entropy 2024, 26(3), 228; https://doi.org/10.3390/e26030228 - 2 Mar 2024
Viewed by 1509
Abstract
The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering [...] Read more.
The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing. The problem of comparing causal mechanisms among multiple systems is non-trivial, since the causal mechanisms are usually unknown and need to be estimated from data. If we estimate the causal mechanisms from data generated from different systems and directly compare them (the naive method), the result can be sub-optimal. This is especially true if the data generated by the different systems differ substantially with respect to their sample sizes. In this case, the quality of the estimated causal mechanisms for the different systems will differ, which can in turn affect the accuracy of the estimated similarities and differences among the systems via the naive method. To mitigate this problem, we introduced the bootstrap estimation and the equal sample size resampling estimation method for estimating the difference between causal networks. Both of these methods use resampling to assess the confidence of the estimation. We compared these methods with the naive method in a set of systematically simulated experimental conditions with a variety of network structures and sample sizes, and using different performance metrics. We also evaluated these methods on various real-world biomedical datasets covering a wide range of data designs. Full article
(This article belongs to the Special Issue Information-Theoretic Causal Inference and Discovery)
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