Inferring causal relationship between spatio-temporal processes based on tail-descriptive estimands

3125 

Contributed Abstract 

Paper 

Zipei Geng (1), Jordan Richards (2), Raphael Huser (1), Marc Genton (1)

(1) King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, (2) University of Edinburgh, Edinburgh, UK

Jordan Richards  
University of Edinburgh

Raphael Huser  
King Abdullah University of Science and Technology

Marc Genton  
King Abdullah University of Science and Technology

Zipei Geng  
King Abdullah University of Science and Technology

Zipei Geng  
N/A

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 contemporaneous and lagged estimands 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 structure in the extremes of 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

Spatio-temporal statistics