[{"year":"2025","citation":{"apa":"Zapata Gonzalez, D. R., Meyer, M., &#38; Müller, O. (2025). <i>Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems</i>. European Conference on Information Systems, Amman, Jordan.","bibtex":"@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }","short":"D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.","mla":"Zapata Gonzalez, David Ricardo, et al. <i>Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems</i>. 2025.","ieee":"D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems,” presented at the European Conference on Information Systems, Amman, Jordan, 2025.","chicago":"Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems,” 2025.","ama":"Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. In: ; 2025."},"title":"Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems","main_file_link":[{"url":"https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/"}],"conference":{"location":"Amman, Jordan","end_date":"18.06.2025","start_date":"16.06.2025","name":"European Conference on Information Systems"},"date_updated":"2025-07-22T06:30:37Z","author":[{"first_name":"David Ricardo","full_name":"Zapata Gonzalez, David Ricardo","id":"105506","last_name":"Zapata Gonzalez"},{"last_name":"Meyer","full_name":"Meyer, Marcel","id":"105120","first_name":"Marcel"},{"first_name":"Oliver","last_name":"Müller","id":"72849","full_name":"Müller, Oliver"}],"date_created":"2025-07-21T07:52:03Z","abstract":[{"text":"Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare \r\ndifferent causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications.","lang":"eng"}],"status":"public","type":"conference","keyword":["Causal Machine Learning","Causality in Time Series","Causal Discovery","Human-Machine  Collaboration"],"language":[{"iso":"eng"}],"_id":"60680","user_id":"72849","department":[{"_id":"196"}]},{"keyword":["Algorithm aversion","Time series","Decision making","Advice taking","Forecasting"],"language":[{"iso":"eng"}],"_id":"37312","user_id":"51271","department":[{"_id":"196"}],"abstract":[{"text":"Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.","lang":"eng"}],"status":"public","type":"conference","publication":"Hawaii International Conference on System Sciences","title":"Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time","main_file_link":[{"url":"https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f","open_access":"1"}],"conference":{"name":"Hawaii International Conference on System Sciences"},"date_updated":"2024-01-10T09:52:59Z","oa":"1","author":[{"first_name":"Dirk","last_name":"Leffrang","orcid":"0000-0001-9004-2391","full_name":"Leffrang, Dirk","id":"51271"},{"last_name":"Bösch","full_name":"Bösch, Kevin","first_name":"Kevin"},{"id":"72849","full_name":"Müller, Oliver","last_name":"Müller","first_name":"Oliver"}],"date_created":"2023-01-18T10:53:51Z","year":"2023","citation":{"apa":"Leffrang, D., Bösch, K., &#38; Müller, O. (2023). Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. <i>Hawaii International Conference on System Sciences</i>. Hawaii International Conference on System Sciences.","mla":"Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” <i>Hawaii International Conference on System Sciences</i>, 2023.","bibtex":"@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii International Conference on System Sciences}, author={Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}, year={2023} }","short":"D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on System Sciences, 2023.","ama":"Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. In: <i>Hawaii International Conference on System Sciences</i>. ; 2023.","chicago":"Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In <i>Hawaii International Conference on System Sciences</i>, 2023.","ieee":"D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii International Conference on System Sciences, 2023."}},{"main_file_link":[{"url":"https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf"}],"conference":{"name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021)","start_date":"2021-09-13","end_date":"2021-09-17","location":"Online"},"title":"PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data","author":[{"last_name":"Müller","id":"72849","full_name":"Müller, Oliver","first_name":"Oliver"},{"first_name":"Matthew","last_name":"Caron","id":"60721","full_name":"Caron, Matthew"},{"first_name":"Michael","full_name":"Döring, Michael","last_name":"Döring"},{"first_name":"Tim","full_name":"Heuwinkel, Tim","last_name":"Heuwinkel"},{"full_name":"Baumeister, Jochen","id":"46","last_name":"Baumeister","orcid":"0000-0003-2683-5826","first_name":"Jochen"}],"date_created":"2021-09-16T08:33:04Z","date_updated":"2023-02-28T08:58:24Z","citation":{"ieee":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","chicago":"Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” In <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>, n.d.","ama":"Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. In: <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","short":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.","bibtex":"@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }","mla":"Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","apa":"Müller, O., Caron, M., Döring, M., Heuwinkel, T., &#38; Baumeister, J. (n.d.). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online."},"year":"2021","publication_status":"inpress","language":[{"iso":"eng"}],"keyword":["expected possession value","handball","tracking data","time series classification","deep learning"],"user_id":"60721","department":[{"_id":"196"},{"_id":"172"}],"_id":"24547","status":"public","abstract":[{"lang":"eng","text":"Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time."}],"type":"conference","publication":"8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)"},{"quality_controlled":"1","issue":"1","year":"2020","citation":{"ama":"Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. In: <i>PHM Society European Conference</i>. Vol 5. ; 2020.","chicago":"Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” In <i>PHM Society European Conference</i>, Vol. 5, 2020.","ieee":"O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data,” in <i>PHM Society European Conference</i>, 2020, vol. 5, no. 1.","apa":"Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. <i>PHM Society European Conference</i>, <i>5</i>(1).","bibtex":"@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2020} }","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” <i>PHM Society European Conference</i>, vol. 5, no. 1, 2020.","short":"O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference, 2020."},"intvolume":"         5","date_updated":"2023-09-22T09:13:16Z","author":[{"id":"9557","full_name":"Aimiyekagbon, Osarenren Kennedy","last_name":"Aimiyekagbon","first_name":"Osarenren Kennedy"},{"first_name":"Amelie","last_name":"Bender","full_name":"Bender, Amelie","id":"54290"},{"first_name":"Walter","last_name":"Sextro","id":"21220","full_name":"Sextro, Walter"}],"date_created":"2020-08-11T13:32:40Z","volume":5,"title":"Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data","type":"conference","publication":"PHM Society European Conference","abstract":[{"text":"In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.","lang":"eng"}],"status":"public","_id":"17810","user_id":"9557","department":[{"_id":"151"}],"keyword":["PHM 2019","crack propagation","forecasting","unevenly spaced time series","step ahead prediction","short time series"],"language":[{"iso":"eng"}]}]