Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
This work addresses a critical challenge in causal inference for longitudinal studies, particularly in fields like climate science, by extending instrumental variable methods to handle time-dependent latent confounders, though it appears incremental as it builds on existing IV and RNN techniques.
The authors tackled the problem of causal inference in longitudinal data with latent time-dependent confounders by proposing a novel Time-dependent Instrumental Factor Model (TIFM), which uses RNNs to infer latent instrumental variables and effectively reduces confounding bias, as demonstrated through extensive synthetic dataset evaluations and application to a climate dataset.
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders. Instrumental variable (IV) is a powerful tool for addressing the latent confounders issue, but the traditional IV technique cannot deal with latent time-dependent confounders in longitudinal studies. In this work, we propose a novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal effect estimation from data with latent time-dependent confounders. At each time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN) architecture to infer latent IV, and then uses the inferred latent IV factor for addressing the confounding bias caused by the latent time-dependent confounders. We provide a theoretical analysis for the proposed TIFM method regarding causal effect estimation in longitudinal data. Extensive evaluation with synthetic datasets demonstrates the effectiveness of TIFM in addressing causal effect estimation over time. We further apply TIFM to a climate dataset to showcase the potential of the proposed method in tackling real-world problems.