CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals

TL;DR

This paper introduces CEPAE, a novel autoencoder-based method with entropy penalization for accurate counterfactual inference in time series data, validated on various datasets.

Contribution

The paper proposes CEPAE, a new autoencoder model with entropy penalization, specifically designed for time series counterfactual inference, extending existing autoencoder methods to this setting.

Findings

CEPAE outperforms existing methods on synthetic and real datasets.

Entropy penalization improves disentanglement in latent space.

The approach is validated both theoretically and experimentally.

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, showing that CEPAE generally outperforms the other approaches in the evaluated metrics.