Add causality paper announcement and image

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+title: "xD Team Member Publishes \"Causality for Trustworthy Artificial Intelligence: Status, Challenges, and Perspectives\" in ACM Computing Surveys"
+publish_date: 2024-05-20
+permalink: /news/causality-paper-announcement/
+img_alt_text: Causality for Trustworthy AI
+image: assets/img/news/xd-team-member-publishes-causality-for-trustworthy-artificial-intelligence-status-challenges-and-perspectives-in-acm-computing-surveys.jpg
+image_accessibility: Causality for Trustworthy AI
+---
+<p>
+  We are excited to share “Causality for Trustworthy Artificial Intelligence: Status, Challenges, and Perspectives” published by an xD team member in ACM Computing Surveys. Causal inference is the idea of cause-and-effect; this fundamental area of science can be applied to problem spaces associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what.” The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning (ML) and artificial intelligence (AI) systems have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. This paper aims to provide a comprehensive survey on such studies of causality. The authors provide a detailed taxonomy of causal inference frameworks, methods, and evaluation. The paper also discusses open challenges and approaches for evaluating the robustness of causal inference methods.
+</p>
+<p>
+  Link to paper: <a class="usa-link" href="https://dl.acm.org/doi/10.1145/3665494" target="_blank">Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives | ACM Computing Surveys</a>
+</p>