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 of causal inference within machine learning models. 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 evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.

The workshop's rationale stems from the escalating interest in causal inference and machine learning methodologies within various industrial contexts. This surge in demand underscores the importance for both scholars and practitioners to exchange knowledge and best practices regarding the application of these techniques to tackle real-world challenges. Yet, applying causal machine learning techniques in real-world scenarios presents a range of challenges not addressed in the academic literature. This workshop aims to address the challenges for practical causal machine learning and explore new industry use cases. The workshop will provide a forum for practitioners and researchers to exchange ideas and explore new collaborations. Moreover, this workshop aims to capitalize on the success and achievements of the KDD 2023 Workshop titled "Causal Inference and Machine Learning in Practice".

The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of the underlying mechanisms and to develop more effective solutions to real-world problems.

In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as CausalML and EconML provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases.

Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from a treatment and then prioritize delivering or promoting the preferred experience to those users. An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously. In this paper, we extend standard uplift models to support multiple treatment groups with different costs. We evaluate the performance of the proposed models using both synthetic and real data. We also describe a production implementation of the approach.