Causal Modeling with Stochastic Confounders
This addresses causal inference challenges in observational studies with complex confounders, though it appears incremental by extending existing work on stochastic confounders.
The paper tackles causal inference with interdependent, time-varying latent confounders from sequential observational data, proposing a variational estimation approach that avoids parametric specifications and demonstrates effectiveness on benchmark temporal datasets.
This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space. We estimate causal effects involving latent confounders that may be interdependent and time-varying from sequential, repeated measurements in an observational study. Our approach extends current work that assumes independent, non-temporal latent confounders, with potentially biased estimators. We introduce a simple yet elegant algorithm without parametric specification on model components. Our method avoids the need for expensive and careful parameterization in deploying complex models, such as deep neural networks, for causal inference in existing approaches. We demonstrate the effectiveness of our approach on various benchmark temporal datasets.