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[2602.15546] CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
Summary
The paper introduces CEPAE, a novel approach utilizing Conditional Entropy-Penalized Autoencoders for effective counterfactual inference in time series data, demonstrating superior performance in various datasets.
Why It Matters
Understanding counterfactuals in time series is essential for decision-making across multiple industries, including finance and healthcare. CEPAE offers a new methodology that enhances the accuracy of these inferences, potentially leading to better strategic decisions and outcomes.
Key Takeaways
- CEPAE employs a unique entropy penalization loss to improve data representation.
- The method is validated on synthetic, semi-synthetic, and real-world datasets.
- CEPAE outperforms existing counterfactual inference methods in time series applications.
- The approach is grounded in the Structural Causal Model framework.
- It addresses a critical need for accurate counterfactual analysis in various industries.
Computer Science > Machine Learning arXiv:2602.15546 (cs) [Submitted on 17 Feb 2026] Title:CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals Authors:Tomàs Garriga, Gerard Sanz, Eduard Serrahima de Cambra, Axel Brando View a PDF of the paper titled CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals, by Tom\`as Garriga and 3 other authors View PDF HTML (experimental) Abstract:The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes over time. In this paper, we introduce a new counterfactual inference approach tailored to time series data impacted by market events, which is motivated by an industrial application. Utilizing the abduction-action-prediction procedure and the Structural Causal Model framework, we first adapt methods based on variational autoencoders and adversarial autoencoders, both previously used in counterfactual literature although not in time series settings. Then, we present the Conditional Entropy-Penalized Autoencoder (CEPAE), a novel autoencoder-based approach for counterfactual inference, which employs an entropy penalization loss over the latent space to encourage disentangled data representations. We validate our approach both theoretically and experimentally on synthetic, semi-synthetic, and real-world datasets, sh...
