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Joscha Cüppers

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4 papers

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AAAI Conference 2026 Conference Paper

Causal Discovery from Interval-Based Event Sequences

Lénaïg Cornanguer
Joscha Cüppers
Jilles Vreeken

In this paper we address the problem of discovering causal relationships from observational event sequence data. Existing methods typically assume that events are instantaneous point events, however in many real-world settings, events have duration. For example, in healthcare, a patient's symptoms may persist over a time interval and influence clinical actions while ongoing. To address this, we introduce a causal model for interval-based event sequences that captures rich causal structures, including interactions between events and causal mechanisms that depend on whether other events are ongoing. We prove that our model is identifiable in the limit and present a practical causal discovery algorithm, Niagara, grounded in the algorithmic Markov condition. To select among candidate models, we employ a minimum description length (MDL) criterion, enabling robust inference even with limited data. We validate our approach on synthetic and real data and demonstrate its utility on a real-world medical case study, where it uncovers meaningful causal relationships from noisy, interval-based event data.

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AAAI Conference 2026 Conference Paper

SEQRET: Mining Rule Sets from Event Sequences

Aleena Siji
Joscha Cüppers
Osman Mian
Jilles Vreeken

Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequences. We do so by discovering rules of the form X --> Y where X and Y are sequential patterns. Rules like these are simple to understand and provide a clear description of the relation between the antecedent and the consequent. To discover succinct and non-redundant sets of rules we formalize the problem in terms of the Minimum Description Length principle. As the search space is enormous and does not exhibit helpful structure, we propose the SEQRET method to discover high-quality rule sets in practice. Through extensive empirical evaluation we show that unlike the state of the art, SEQRET ably recovers the ground truth on synthetic datasets and finds useful rules from real datasets.

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NeurIPS Conference 2024 Conference Paper

Causal Discovery from Event Sequences by Local Cause-Effect Attribution

Joscha Cüppers
Sascha Xu
Ahmed Musa
Jilles Vreeken

Sequences of events, such as crashes in the stock market or outages in a network, contain strong temporal dependencies, whose understanding is crucial to react to and influence future events. In this paper, we study the problem of discovering the underlying causal structure from event sequences. To this end, we introduce a new causal model, where individual events of the cause trigger events of the effect with dynamic delays. We show that in contrast to existing methods based on Granger causality, our model is identifiable for both instant and delayed effects. We base our approach on the Algorithmic Markov Condition, by which we identify the true causal network as the one that minimizes the Kolmogorov complexity. As the Kolmogorov complexity is not computable, we instantiate our model using Minimum Description Length and show that the resulting score identifies the causal direction. To discover causal graphs, we introduce the Cascade algorithm, which adds edges in topological order. Extensive evaluation shows that Cascade outperforms existing methods in settings with instantaneous effects, noise, and multiple colliders, and discovers insightful causal graphs on real-world data.

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AAAI Conference 2024 Conference Paper

Discovering Sequential Patterns with Predictable Inter-event Delays

Joscha Cüppers
Paul Krieger
Jilles Vreeken

Summarizing sequential data with serial episodes allows non-trivial insight into the data generating process. Existing methods penalize gaps in pattern occurrences equally, regardless of where in the pattern these occur. This results in a strong bias against patterns with long inter-event delays, and in addition that regularity in terms of delays is not rewarded or discovered---even though both aspects provide key insight. In this paper we tackle both these problems by explicitly modeling inter-event delay distributions. That is, we are not only interested in discovering the patterns, but also in describing how many times steps typically occur between their individual events. We formalize the problem in terms of the Minimum Description Length principle, by which we say the best set of patterns is the one that compresses the data best. The resulting optimization problem does not lend itself to exact optimization, and hence we propose Hopper to heuristically mine high quality patterns. Extensive experiments show that Hopper efficiently recovers the ground truth, discovers meaningful patterns from real-world data, and outperforms existing methods in discovering long-delay patterns.

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