Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning (2505.05192v2)

Abstract: Estimating long-term causal effects by combining long-term observational and short-term experimental data is a crucial but challenging problem in many real-world scenarios. In existing methods, several ideal assumptions, e.g. latent unconfoundedness assumption or additive equi-confounding bias assumption, are proposed to address the latent confounder problem raised by the observational data. However, in real-world applications, these assumptions are typically violated which limits their practical effectiveness. In this paper, we tackle the problem of estimating the long-term individual causal effects without the aforementioned assumptions. Specifically, we propose to utilize the natural heterogeneity of data, such as data from multiple sources, to identify latent confounders, thereby significantly avoiding reliance on idealized assumptions. Practically, we devise a latent representation learning-based estimator of long-term causal effects. Theoretically, we establish the identifiability of latent confounders, with which we further achieve long-term effect identification. Extensive experimental studies, conducted on multiple synthetic and semi-synthetic datasets, demonstrate the effectiveness of our proposed method.

• The paper proposes a novel method for estimating long-term individual causal effects in observational data by learning identifiable latent representations to address latent confounders.
• This new approach utilizes variational inference guided by auxiliary variables to recover hidden confounders, unlike previous methods that rely on restrictive assumptions.
• Experiments on synthetic datasets demonstrate that the proposed method outperforms existing approaches, offering improved robustness for applications in areas like healthcare and economics.

Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning

The paper "Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning" addresses a pertinent issue in the field of causal inference: estimating long-term causal effects in scenarios where latent confounders are present. The significance of determining causal effects from observational data is paramount because conducting randomized control trials over extended periods is impractical. Though existing frameworks attempt to simplify this complex problem via assumptions such as latent unconfoundedness and additive equi-confounding bias, such conditions are hardly applicable in real-world datasets.

The authors propose a novel approach to long-term causal inference that circumvents the need for these idealized assumptions. They leverage the inherent heterogeneity in data, such as from multiple sources, to identify latent confounders. By doing this, they develop a latent representation learning-based estimator that can more accurately estimate long-term causal effects. This approach is characterized by utilizing variational inference to recover latent confounders, guided by auxiliary variables that ensure identifiability. The proposed methodology stands as a robust alternative because it rests on a more general setting as opposed to previous models, thus broadening its applicability.

Theoretical guarantees presented in the paper establish the identifiability of latent confounders, which are crucial for achieving long-term effect identification. By relying on an identifiable representation learning process, the model ensures that the latent variables affecting both the treatment and outcomes can be effectively recovered. Identifiability allows the causal effect model to capture hidden confounders more accurately, which is fundamental in preventing bias in causal effect estimates.

Experimental validation on synthetic and semi-synthetic datasets reveals that the authors' approach outperforms existing methods, specifically those burdened by restrictive assumptions regarding confounders. Across synthetic datasets designed to follow different confounding assumptions, the proposed method consistently shows comparable or better performance, demonstrating its adaptability and robustness.

From an applied perspective, this paper suggests substantial improvements toward estimating individual long-term causal effects, which is critical in fields such as healthcare and economics where decisions must often rely on observational data. The results open avenues for research into more sophisticated causal inference models that leverage identifiable representations of complex data structures.

In summary, this research contributes significantly to the body of work in causal inference by providing a method that accommodates real-world data complexity and uncertainty. Future work might explore the application of similar methodologies to other types of latent variable models, potentially expanding the scope of identifiable representation learning in artificial intelligence and data science.