Causal inference for process understanding in Earth sciences

Abstract

There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth system to identify where causal inference may be most tractable and useful to address problems in Earth science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed portion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system's state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples that causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties.

Evaluating Digital Agriculture Recommendations with Causal Inference

“…Causal inference with observational data has been the subject of recent work across diverse disciplines, including ecology (Arif and MacNeil 2022), public policy (Fougère and Jacquemet 2019), and Earth sciences (Massmann, Gentine, and Runge 2021;Runge et al 2019). In agriculture, it has been used to identify and estimate the effect of agricultural practices on various agro-environmental metrics (Qian and Harmel 2016;Deines, Wang, and Lobell 2019;Giannarakis et al 2022a,b).…”

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

“…Thus, an observational causal inference framework [46] can fill this gap by emulating the experiment we would have liked to run [30]. Causal inference with observational data has been the subject of recent work across diverse disciplines, including ecology [3], public policy [23,24], and Earth sciences [42,47,50]. In agriculture, it has been used to identify and estimate the effect of agricultural practices on various agro-environmental metrics [48,18,27].…”