Results 71 to 80 of about 644,547 (283)

Dynamic Structural Causal Models

We study a specific type of SCM, called a Dynamic Structural Causal Model (DSCM), whose endogenous variables represent functions of time, which is possibly cyclic and allows for latent confounding. As a motivating use-case, we show that certain systems of Stochastic Differential Equations (SDEs) can be appropriately represented with DSCMs. An immediate
Boeken, Philip, Mooij, Joris M.
openaire   +2 more sources

Causal Discovery in Linear Structural Causal Models with Deterministic Relations

, 2021
Accepted at 1st Conference on Causal Learning and Reasoning (CLeaR 2022)
Yang, Yuqin, Nafea, Mohamed, Ghassami, AmirEmad, Kiyavash, Negar   +3 more
openaire   +2 more sources

Linking neurogenesis, oligodendrogenesis, and myelination defects to neurodevelopmental disruption in primary mitochondrial disorders

FEBS Letters, EarlyView.
Mitochondrial remodeling shapes neural and glial lineage progression by matching metabolic supply with demand. Elevated OXPHOS supports differentiation and myelin formation, while myelin compaction lowers mitochondrial dependence, revealing mitochondria as key drivers of developmental energy adaptation.
Sahitya Ranjan Biswas, Porter L. Tomsick, Alicia M. Pickrell, Paul D. Morton   +3 more
wiley   +1 more source

From conceptualising to modelling structural determinants and interventions in HIV transmission dynamics models: a scoping review and methodological framework for evidence-based analyses

BMC Medicine
Background Including structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that
James Stannah, Jorge Luis Flores Anato, Michael Pickles, Joseph Larmarange, Kate M. Mitchell, Adelina Artenie, Kostyantyn Dumchev, Serge Niangoran, Lucy Platt, Fern Terris-Prestholt, Aditya Singh, Jack Stone, Peter Vickerman, Andrew Phillips, Leigh Johnson, Mathieu Maheu-Giroux, Marie-Claude Boily   +16 more
doaj   +1 more source

Standardizing Structural Causal Models

Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correlations in SCM data tend to increase along the causal ordering. Several popular algorithms exploit these artifacts, possibly leading to conclusions that do not generalize to ...
Ormaniec, Weronika, Sussex, Scott, Lorch, Lars, Schölkopf, Bernhard, Krause, Andreas   +4 more
openaire   +2 more sources

Congenial Causal Inference with Binary Structural Nested Mean Models

, 2017
Structural nested mean models (SNMMs) are among the fundamental tools for inferring causal effects of time-dependent exposures from longitudinal studies.
Richardson, Thomas S., Robins, James M., Wang, Linbo   +2 more
core  

AAA+ protein unfoldases—the Moirai of the proteome

FEBS Letters, EarlyView.
AAA+ unfoldases are essential molecular motors that power protein degradation and disaggregation. This review integrates recent cryo‐electron microscopy (cryo‐EM) structures and single‐molecule biophysical data to reconcile competing models of substrate translocation.
Stavros Azinas, Marta Carroni
wiley   +1 more source

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Machine Learning: Science and Technology
Causality is innate to the determination of the fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modelling to understand structure-functionality connections is extremely
Ayana Ghosh, Saurabh Ghosh
doaj   +1 more source

Higher Algebraic K-Theory of Causality

Entropy
Causal discovery involves searching intractably large spaces. Decomposing the search space into classes of observationally equivalent causal models is a well-studied avenue to making discovery tractable.
Sridhar Mahadevan
doaj   +1 more source

Causal inference using invariant prediction: identification and confidence intervals

, 2015
What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment.
Bühlmann, Peter, Meinshausen, Nicolai, Peters, Jonas   +2 more
core