________________________________________________________________________________________________ Titel: Workshop @ ECML PKDD 2023: Uplift modeling and causal machine learning for operational decision-making Bericht: Dear colleagues,
Please consider participating in the following workshop at this year's ECML PKDD conference: Uplift modeling and causal machine learning for operational decision-making Introduction 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 ODM, to present and discuss recent developments, to identify open issues and to form a community and foster future initiatives. Call for papers We invite original contributions related to uplift modeling and causal machine learning for operational decision-making. Both methodological and application-oriented submissions are welcomed. Accepted papers will be published in a Springer volume of ECML/PKDD'23 workshop proceedings. The list of topics includes but is not limited to: * Novel uplift modeling and causal machine learning techniques * Learning to rank for uplift modeling and causal prediction * Procedures and measures for evaluation * Practical causal discovery and causal effect estimation under biased treatment assignment * Beyond binary treatments and binary outcomes: continuous, multi- and high-dimensional treatments, continuous or complex (multiple, time-dependent, etc.) outcomes * Cost-sensitive uplift modeling and causal machine learning * Practical applications and case studies * Descriptions of datasets and benchmarking experiments Important dates: * Abstract/paper submission deadline: June 30th * Acceptance notification: July 15th * Workshop @ ECML PKDD: Friday September 22nd, in the afternoon. All information on the workshop can be found on https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fupliftworkshop.ipipan.waw.pl%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C833c6a890d3c419d888508db63496b33%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638212939215438400%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=mAlEuILqRzmIZ5vUg3SOF67Xax7X0AQaW4iKoKamZSM%3D&reserved=0. ECML PKDD 2023 takes place from September 18 to 22 2023 in Turin, Italy. Conference website: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2F2023.ecmlpkdd.org%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C833c6a890d3c419d888508db63496b33%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638212939215438400%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=XSh7qDItgBrInQGSu2cHj8C%2FPGKiIpgReoYeLJ5QdFI%3D&reserved=0. Thank you, Kind regards, Wouter Prof.dr.ir. 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