Call for Paper

Important Dates

All deadlines are at 11:59 PM AoE.

• April 26th, 2025: CMT submission portal opens
• May 8th, 2025: Abstract submission deadline
• June 8th, 2025: Workshop paper submission deadline
• July 1st, 2025: Paper decision notifications
• July 14th, 2025: Camera-ready deadline
• August 6th, 2025: Workshop

Submission Link

• CMT submission portal: https://cmt3.research.microsoft.com/CMLKDD2025/

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

Aim and Scope

This workshop aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference with machine learning. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for applying and evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with machine learning algorithms.

We welcome papers on a variety of topics, including but not limited to the following:

• Industry use cases where causal inference and machine learning are used in practice
• Challenges and opportunities for using causal inference and machine learning in industry settings
• Case studies of using causal machine learning with large-scale data
• Techniques for incorporating causal inference into machine learning models
• Methodologies for evaluating causal machine learning models in practice
• Analysis of the trustworthiness, robustness, and fairness of causal machine learning methods
• Methodologies and applications that connect causal inference with GenAI or LLM; example applications include using GenAI or LLM to assist causal inference or using causal approaches for GenAI or LLM evaluation.

We encourage submissions from researchers and practitioners working in industry, government, and academia. We welcome papers that present new research results, works in progress, or case studies that showcase the application of causal inference and machine learning techniques to real-world problems.

All submissions will be peer-reviewed by the program committee, and accepted papers will be presented as contributed talks or posters during the workshop.

Submission and Formatting Instructions

• Submissions are single-blind—author names and affiliations should be listed.
• Submissions are limited to 6 pages (excluding references), must be in PDF, and use the ACM Conference Proceeding template (two-column format).
• The recommended setting for Latex documents is: \documentclass[sigconf, review]{acmart}.
• Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit.
• The supplementary material should be included in the same PDF file as the main manuscript. The main body of the paper should be self-contained since reviewers are not required to read the supplementary material. The supplementary material will not be included in the proceedings.
• Submissions violating these formatting requirements will be desk-rejected.
• The Word template guideline can be found here.
• The Latex/overleaf template guideline can be found here.

For any questions or inquiries, please contact the workshop organizers at jpan2@snapchat.com and zyzheng@berkeley.edu. We look forward to your submissions!