Using Causal Machine Learning to Track Population Change With Participatory Science Data

Music City Center 

The increasing volumes of species observation data being collected by participatory (or citizen) science projects around the world have great potential for monitoring populations and helping to identify the drivers of population change. However, to realize this potential requires methods that can 1) estimate heterogenous spatial patterns of population change that arise when multiple drivers (e.g. change in land use and climate) affect species populations simultaneously, and 2) control for confounding sources of inter-annual variation common in large-scale crowdsourced spatiotemporal datasets. In this presentation we investigate the use of multi-arm causal machine learning models designed for Conditional Average Treatment Effect (CATE) estimation to address these challenges. Using a simulation study and data from the citizen-science project eBird, we assess performance estimating spatially varying inter-annual trajectories in population size and identifying drivers of population change in the face of real-world confounding. We discuss results showing how this approach can recover heterogenous trends and discuss outstanding challenges.