Inferring causal relationships between spatio-temporal processes using tail-descriptive estimands

Oregon Convention Center 

We propose a latent spatio-temporal causal model for a class of causal estimands that go beyond the conditional expectation. In particular, we focus on estimands for contemporaneous and lagged effects that serve as descriptors of the tail behaviour of the predictive distribution of the underlying spatio-temporal process. Under mild sufficient conditions, we theoretically validate the correctness of causal interpretation and further prove: i) the identifiability of causal effects using the full observational distribution; and ii) the consistency of our model estimator. We provide a simulation study to illustrate the correctness of our asymptotic consistency theorem and showcase the advantages of using a causal estimand, that focuses on the tails, over the traditional conditional expectation. Finally, we apply our framework to quantify causal spatio-temporal structures in U.S. wildfire and air quality data.

air quality data

causal inference

extreme event

spatio-temporal process

tail-descriptive estimand

wildfire data 

Section on Statistics and the Environment