David Kaltenpoth
Post-doctoral Researcher
Exploratory Data Analysis
CISPA Helmholtz Center for Information Security
CISPA D2, Room 0.03
Im Oberem Werk 1
66386 St. Ingbert, Germany
Im Oberem Werk 1
66386 St. Ingbert, Germany
david.kaltenpoth@cispa.de
David Kaltenpoth is a postdoc who works on causal inference under realistic conditions, such as confounding, selection bias, or non-i.i.d. data.
David obtained his Ph.D. in Computer Science from Saarland University on November 25th, 2024. His thesis, 'Don't Confound Yourself: Causality from Biased Data,' was awarded the Helmholtz AI Dissertation Award 2024.
David obtained his Master of Science in Mathematics from the Ludwigs-Maximilian Universität München in 2016. He joined the EDA group in June 2016 for a Research Immersion lab, stayed for his Ph.D., and is now a postdoc.
Publications
2024 | |
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Identifying Confounding from Causal Mechanism Shifts. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2024. (27.6% acceptance rate)  |
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Don't Confound Yourself: Causality from Biased Data. Dissertation, Saarland University, 2024. (Helmholtz AI Best Dissertation Award) |
2023 | |
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Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), AUAI, 2023. (31.2% acceptance rate) |
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Nonlinear Causal Discovery with Latent Confounders. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2023. (27.9% acceptance rate) |
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Identifying Selection Bias from Observational Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp 8177-8185, AAAI, 2023. (oral presentation, 10.8% acceptance rate; 19.6% overall) |
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Learning Causal Models under Independent Changes. In: Proceedings of Neural Information Processing Systems (NeurIPS), PMRL, 2023. (26.1% acceptance rate) |
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Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. In: Proceedings of the 26nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2023. (29% acceptance rate) |
2022 | |
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Discovering Invariant and Changing Mechanisms from Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 1242-1252, ACM, 2022. (15.0% acceptance rate)  |
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Regret-based Federated Causal Discovery. In: Proceedings of the ACM SIGKDD Workshop on Causal Discovery, PMLR, 2022. |
2020 | |
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Discovering Functional Dependencies from Mixed-Type Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2020. (16.8% acceptance rate) |
2019 | |
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We Are Not Your Real Parents: Telling Causal From Confounded by MDL. In: SIAM International Conference on Data Mining (SDM), SIAM, 2019. (22.9% acceptance rate) |