Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data (2006.10833v3)

Abstract: On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following this approach. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding.

• The paper presents ACD, a novel framework that infers causal relations by exploiting shared dynamics across different time-series samples.
• It implements a probabilistic model using an encoder-decoder architecture to efficiently capture causal structures and improve inference accuracy.
• ACD robustly handles noise and hidden confounders, outperforming traditional methods on benchmark datasets like Kuramoto and particles.

Overview of Amortized Causal Discovery for Time-Series Data

The paper "Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data" presents a novel framework for inferring causal relations in time-series data by utilizing shared dynamics across samples. This method, termed Amortized Causal Discovery (ACD), is designed to address the limitations of traditional causal discovery methods that refit causal models independently for each sample, thereby missing out on potential shared information across samples.

Key Contributions

1. Framework Proposal: The authors propose ACD as a method that learns to infer causal relations across different samples with varying underlying causal graphs but shared dynamics. This novel approach departs from conventional methods that require separate models for different causal structures, failing to exploit the commonalities across samples.
2. Probabilistic Model Implementation: The implementation employs a variational model comprising an encoder and a decoder. The encoder infers a causal graph from the observed time series, while the decoder models the shared dynamics across samples. This separation allows the framework to leverage additional training data from various samples, thus enhancing the causal discovery process.
3. Handling Noise and Hidden Confounding: The paper showcases the robustness of ACD when dealing with noisy data and hidden confounders. It provides methods to extend the variational model to operate efficiently under these common real-world challenges, thus broadening the applicability of the proposed approach.

Experimental Results

The experimental evaluation highlights the efficacy of ACD across several domains:

• Performance Gains: On datasets such as Kuramoto and particles, ACD significantly outperforms existing methodologies in causal discovery accuracy. The model demonstrates remarkable scalability with increasing amounts of training data owing to its design of generalizing across samples by leveraging shared dynamics.
• Noise Robustness: ACD exhibits resilience in the presence of noise, maintaining superior performance levels compared to baselines, even as the noise level increases.
• Hidden Confounders: The paper also examines scenarios with hidden confounders, such as unobserved temperature variables that influence causal relations. The proposed framework effectively models these scenarios, providing insights into latent variable impacts.

Implications and Future Directions

The ACD framework represents a significant advancement in causal discovery from time-series data, offering a solution that amalgamates information across samples through shared dynamics. The approach's handling of noise and hidden variables marks a step forward in addressing common causal inference challenges.

Theoretical and Practical Implications: From a theoretical perspective, the paper highlights the potential of shared dynamics to enhance causal inference, prompting further exploration in both theoretical formulations and more robust models. Practically, this opens new avenues in various fields relying on time-series data, such as neuroscience, finance, and social sciences, where causal relations need to be inferred under practical constraints of noise and confounding factors.

Further Research Directions: As the paper primarily focuses on simulated datasets, an immediate extension could be the application and validation of ACD on real-world datasets, gaining insights into its performance and adaptability in more complex environments. Furthermore, future work may delve into refining the model assumptions and expanding the general framework to accommodate more intricate dynamics and dependencies.

In summary, Amortized Causal Discovery introduces a powerful tool for time-series causal inference, expanding the boundaries of existing methodologies through innovative use of shared dynamic information. Its application has the potential to revolutionize how causal graphs are inferred across diverse and noisy datasets, offering a robust model for the scientific and practical exploration of time-series data.