Accounting for outcome spillover for causal inference with continuous spatiotemporal processes

Music City Center 

Achieving causal inference for processes that generate continuous spatiotemporal point process data is challenging. Current methods rely on data discretization and assuming points do not interact. We demonstrate that, in a highly general parametric setting, causal inference with observational spatiotemporal data in the presence of arbitrary outcome spillover is feasible. To do so, we construct a general framework for novel causal estimands of outcomes of interest using results from point process theory, prove theoretical properties necessary to establish rigorous hypothesis testing and demonstrate practical estimability. Our proposed framework accommodates observational and experimental data, random and non-random treatment mechanisms, a general class of model specifications including those that allow for interaction between points, and state spaces ranging from subsets of $\mathbb{R}^d$ to linear networks. This work is pertinent to applications as diverse as epidemiology and finance, enabling previously impossible causal inference on rich continuous spatiotemporal data.

Causal Inference

Spillover

Point Process

Hawkes Process

Epidemics

Interference 

Section on Statistics in Epidemiology