Benchmarking conditioners in Liang–Kleeman Information Flow: application to land–atmosphere interactions in humid forests
- 1Nanjing University of Information Science and Technology (Nanjing, China)
Abstract
The Liang–Kleeman Information Flow (LKIF) framework has been increasingly used to identify causality in Earth–system dynamics in recent years. Yet, uncertainties remain regarding how the causal graph changes when mediators and confounders (conditioners) are introduced, i.e., divergence between the bivariate and conditioned forms. A controlled synthetic experiment shows that LKIF preserves correct causal directions under hidden confounding, while differences between bivariate and multivariate estimates still occur. We characterize these differences using time-varying LKIF and introduce several quantifiers, including Conditioner Dominance Index, Moderation Gain, Conditioning Pressure, and Convergence Rate, to evaluate the contributions of conditioners. This is illustrated in an application to soil moisture (SM) and vapor pressure deficit (VPD) couplings with vegetation (leaf area index (LAI) and gross primary productivity (GPP)) in humid forest regions of Southeast China and the Amazon Basin, using satellite-derived and reanalysis data over 1981–2018. We find that the influence of SM on LAI remains consistently stable and shows little sensitivity to the selected conditioners, whereas its influence on GPP and that of VPD on vegetation exhibit substantial divergence between bivariate and multivariate estimates. This divergence peaks under compound dry–hot conditions, with VPD and temperature emerging as dominant conditioners. These results demonstrate that bivariate and multivariate LKIF can yield markedly different conclusions across time and space, especially under compound environmental stress. More importantly, even for the same variables in a causal graph, the same conditioners can affect the temporal evolution of the causal relationships very differently at different locations. This highlights how stationary assumptions may oversimplify causal structure by masking relevant temporal transitions, particularly under compound environmental stress.
Versions
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BibTeX
@article{siddique2026benchmarking,
title = {{Benchmarking conditioners in Liang–Kleeman Information Flow: application to land–atmosphere interactions in humid forests}},
author = {Fareeha Siddique and Daniel Fiifi Tawia Hagan and Guojie Wang and David Docquier and Stéphane Vannitsem},
journal = {Earth System Dynamics},
year = {2026},
month = oct,
doi = {10.5194/esd-17-1551-2026},
url = {https://esd.copernicus.org/articles/17/1551/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Benchmarking conditioners in Liang–Kleeman Information Flow: application to land–atmosphere interactions in humid forests
AU - Fareeha Siddique
AU - Daniel Fiifi Tawia Hagan
AU - Guojie Wang
AU - David Docquier
AU - Stéphane Vannitsem
PY - 2026
DA - 2026/10/20
JO - Earth System Dynamics
DO - 10.5194/esd-17-1551-2026
UR - https://esd.copernicus.org/articles/17/1551/2026/
AB - The Liang–Kleeman Information Flow (LKIF) framework has been increasingly used to identify causality in Earth–system dynamics in recent years. Yet, uncertainties remain regarding how the causal graph changes when mediators and confounders (conditioners) are introduced, i.e., divergence between the bivariate and conditioned forms. A controlled synthetic experiment shows that LKIF preserves correct causal directions under hidden confounding, while differences between bivariate and multivariate estimates still occur. We characterize these differences using time-varying LKIF and introduce several quantifiers, including Conditioner Dominance Index, Moderation Gain, Conditioning Pressure, and Convergence Rate, to evaluate the contributions of conditioners. This is illustrated in an application to soil moisture (SM) and vapor pressure deficit (VPD) couplings with vegetation (leaf area index (LAI) and gross primary productivity (GPP)) in humid forest regions of Southeast China and the Amazon Basin, using satellite-derived and reanalysis data over 1981–2018. We find that the influence of SM on LAI remains consistently stable and shows little sensitivity to the selected conditioners, whereas its influence on GPP and that of VPD on vegetation exhibit substantial divergence between bivariate and multivariate estimates. This divergence peaks under compound dry–hot conditions, with VPD and temperature emerging as dominant conditioners. These results demonstrate that bivariate and multivariate LKIF can yield markedly different conclusions across time and space, especially under compound environmental stress. More importantly, even for the same variables in a causal graph, the same conditioners can affect the temporal evolution of the causal relationships very differently at different locations. This highlights how stationary assumptions may oversimplify causal structure by masking relevant temporal transitions, particularly under compound environmental stress.
SN - 2190-4987
ER -