Topic

Uplift modeling (UM) and causal machine learning (CML) for operational decision making (ODM) concerns the discovery and estimation of causal effects from data for optimizing, automating or customizing operational decision-making. The field receives a growing interest from both academics and industry practitioners, with applications in marketing, process management, pricing, medicine, machine maintenance, operations management, human resources, etc.

UM & CML entail a diverse range of specialized data-driven methods, drawing from both the fields of causal inference and machine learning. These methods can either learn (1) from experimental data obtained through randomized controlled trials (RCT), which in some settings is commonly available (e.g., A/B test data in marketing), or (2) from observational data, which is gathered by observing ongoing processes and operations subject to the current decision-making policy. Uplift modeling and causal machine learning extend upon predictive modeling (i.e., supervised learning) and involve additional challenges and complexity, for instance, related to addressing selection bias or evaluating counterfactual predictions. The approaches require special methodology to address issues such as the fundamental problem of causal inference (unobservability of counterfactual outcomes). The field differs from other areas of causal discovery by focusing on practical applications and business problems. A large number of open research questions and practical challenges towards adopting uplift modeling and causal machine learning in practice are still to be addressed and the domain would benefit from further formalization.

This workshop aims at bringing together, for the second time at ECML/PKDD, researchers and practitioners working on UM & CML for DM, to present and discuss recent developments, to identify open issues and to form a community and foster future initiatives.

The workshop is a continuation of the ECML/PKDD'22 Uplift Modeling Tutorial and Workshop. The website of the 2022 edition is available here.

Submission guidelines

Submitions should be made HERE.

Various types of submissions are encouraged: full papers and extended abstracts. We also make it possible to directly submit a poster you intend to present at the workshop.

Papers and abstracts should adhere to the general paper formatting guidelines as provided on the ECML/PKDD2023 conference website in the Paper Format section.

Authors of accepted submissions will be invited to present their paper in an oral presentation (accompanied by a poster) or a poster presentation and will have an option of publishing the paper in Springer post-proceedings volume.

Please note that at least one author of each accepted paper must have a full registration and be in Turin to present the paper.

Keynote Presentation

About the speaker: Gaël Varoquaux is a research director working on data science at Inria (French Computer Science National research) where he leads the Soda team on computational and statistical methods to understand health and society with data. Varoquaux is an expert in machine learning, with an eye on applications in health and social science. He develops tools to make machine learning easier, suited for real-life, messy data. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. He currently develops data-intensive approaches for epidemiology and public health, and worked for 10 years on machine learning for brain function and mental health. Varoquaux has a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris.

2022 Uplift Modeling Tutorial and Workshop

The workshop is a continuation of the ECML/PKDD'22 Uplift Modeling Tutorial and Workshop. The website of the 2022 edition is available here.