Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
This addresses a crucial challenge in real-world causal inference for applications where existing assumptions are often violated, though it appears incremental as it builds on prior methods by relaxing assumptions.
The paper tackles the problem of estimating long-term individual causal effects without relying on ideal assumptions like latent unconfoundedness, by using data heterogeneity to identify latent confounders and proposing a latent representation learning-based estimator. It demonstrates effectiveness through experiments on synthetic and semi-synthetic datasets.
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.