Causal Inference for Sequential Settings under Interference and Latent Confounding
This work provides a novel method for estimating causal effects in panel data with interference and confounding, which is relevant for social science and epidemiology.
The paper addresses causal inference under outcome interference and latent confounding in sequential observational settings, proposing a computationally efficient method based on Maximum Pseudo-Likelihood Estimation. The method achieves non-asymptotic consistency and is validated on synthetic data and a real-world case study on COVID-19 death rates.
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also impacted through an external field capturing the effects of its treatment as well as latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudo-Likelihood Estimation (MPLE) for learning the model parameters. Under mild assumptions, we establish non-asymptotic consistency for parameter estimation and show this translates to faithful estimation of causal quantities of interest after sampling from the learned model. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.