Kdd Causal Inference. 3rd Workshop on Causal Inference and Machine Learning in Pra
3rd Workshop on Causal Inference and Machine Learning in Practice Schedule Room 601, Metro Toronto Convention Centre, 255 Front St W, Toronto, ON M5V 2W6, Canada map Date: Call for Nominations: ACM SIGKDD 2025 Innovation, Service Award, and Rising Star Award Call for Nominations: 2025 SIGKDD Dissertation Award Call for Nominations: 2025 SIGKDD Test Join us at the 3rd Workshop on Causal Inference and Machine Learning in Practice at #KDD2025 in Toronto—share your insights and Conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal analysis. As causal The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application Randomized experiments: The gold standard for causal inference Introduction to causal inference, counterfactual frameworks and intuition We begin by motivating the use of causal inference Call for Nominations: ACM SIGKDD 2025 Innovation, Service Award, and Rising Star Award Call for Nominations: 2025 SIGKDD Dissertation Award Call for Nominations: 2025 SIGKDD Test Abstract A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of However, there is limited research on variance reduction techniques under the context of adver-tising measurement with causal inference estimators. Moreover, this workshop aims to capitalize on the Aim and Scope This workshop aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference with machine learning. To tackle such questions, Causal Inference with Large-scale Observational Data in Practice: Industrial Tooling and Use Cases at Snap and Airbnb Abstract Product launches and iterations are a critical driver of Join us at the 3rd Workshop on Causal Inference and Machine Learning in Practice at #KDD2025 in Toronto—share your insights and Causal Inference with Latent Variables: Recent Advances & Future Prospectives 1Yaochen Zhu 1Yinhan He 2Jing Ma 1 Mengxuan Hu 1Sheng Li 1Jundong Li 🎤 Invited Speaker Announcement (3/3) We’re thrilled to announce that Wenjing Zheng and Jeffrey Wong will be joining us as invited speakers at the Causal Inference and Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. At a high-level, this framework Song Jiang, University of California, Los Angeles Our model CF-GODE, is a causal model that estimates continuous-time counterfactual outcomes in multi-agent It’s a blessing that today we have events like KDD’s Causal Inference and Machine Learning in Practice workshop, where researchers Workshop Date Time Data Science in India(fully virtual) Sunday, August 25 5:30 AM – 9:30 AM(9:00 AM – 1:00 PM IST) RelKD 2024: The Second International Workshop on Resource Quantifying the causal efect of these thresholds on customers is crucial for efective marketing strategy design. DoWhy is based on 📢 Announcing the 3rd Workshop on Causal Inference and Machine Learning in Practice 📍 Toronto, Canada | 🗓️ Wednesday, August 6, 2025 | 🌐 Part of #KDD2025 We’re excited In this paper, we propose a novel framework— Causal Inference under Spillover Efects in Hypergraphs (HyperSCI)—to model high-order interference. While regression discontinuity design is a common method for such causal . This tutorial will introduce participants to concepts in causal Abstract The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to We are happy to announce the 2nd Workshop on Causal Inference and Machine Learning in Practice to be held at the KDD 2024 Conference, Barcelona, Spain, August 25-26, The workshop will provide a forum for practitioners and researchers to exchange ideas and explore new collaborations. How to marry DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. Lit-erature in advertising measurement Abstract The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to Keywords Interpratablity, explainability, causal inference, counterfac-tuals, machine learning Aim and Scope The workshop aims to bring together researchers and practitioners from academia and industry to share their experiences and insights on applying causal The situation is complicated by the fact that, until recently, the field lacked a unified, publicly available, and configurable platform that supports all major causal inference tasks, including We first motivate the use of causal inference through examples in domains such as recommender systems, social media datasets, health, education and governance.