Revisiting impacts of MJO on soil moisture: a causality perspective

Bueso, Diego; Piles, María; Székely, Endre; Camps‐Valls, Gustau

doi:10.1002/essoar.10501866.1

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compare against the non-linear (kernel-based) generalization of Granger causality (GC) (Bueso et al 2019) and naively implement ANM and LNc: i.e. we do not take into account that data are time-ordered in any way.…”

Section: Results On Time Series Datamentioning

confidence: 99%

Learning latent functions for causal discovery

Diaz

Varando

Johnson

et al. 2023

Mach. Learn.: Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions. Recent causal discovery methods focused on estimating the latent space of the data to get around a lack of causal sufficiency or additivity constraints. However, estimating the latent space significantly increases model complexity, compromising causal identifiability and making it hard to compare models that correspond to different causal hypotheses. We propose a kernel, non-parametric latent-space modelling approach and deal with the difficulty of comparing causal directions by measuring and controlling for the level of causal assumption fulfilment. We introduce a latent noise causal inference framework to estimate latent factors associated with the hypothesized causal direction by optimizing a loss function with kernel independence criteria. We extend the framework to work with time series using an additional time-dependent kernel regularizer. We discuss the additivity assumption and model complexity and give empirical evidence of performance in a wide range of synthetic and real causal discovery problems.

show abstract

Section: Results On Time Series Datamentioning

confidence: 99%