Results 31 to 40 of about 644,547 (283)

A Linear “Microscope” for Interventions and Counterfactuals

Journal of Causal Inference, 2017
This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams.
Pearl Judea
doaj   +1 more source

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

, 2020
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem.
Chernozhukov, Victor, Wuthrich, Kaspar, Zhu, Yinchu   +2 more
core   +1 more source

Two Faces of NOTCH1 in Childhood Lymphoblastic T‐Cell Neoplasia: Prognostic Divergence of Mutational and Structural Aberrations

Pediatric Blood &Cancer, EarlyView.
ABSTRACT In pediatric patients, T‐cell lymphoblastic lymphoma (T‐LBL) survival exceeds 80%. Relapse remains associated with limited curative options. Frontline treatment is largely extrapolated from T‐cell acute lymphoblastic leukemia (T‐ALL) treatment, reflecting the ongoing debate, whether both entities represent distinct diseases or variants within ...
Marie C. Heider, Amelie Alfert, Marc Hotfilder, Birgit Burkardt, Marcel te Vrugt   +4 more
wiley   +1 more source

Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models

Ecological Informatics
Understanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making.
Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh   +7 more
doaj   +1 more source

Organoids in pediatric cancer research

FEBS Letters, EarlyView.
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley   +1 more source

Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

, 2003
In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics,
Dawid A. P., Gill R. D., Lok J. J., Robins J. M., Robins J. M., Robins J. M., Robins J. M., Robins J. M., Robins J. M., Robins J. M., Rubin D. B., Van der Laan M. J., Van der Vaart A. W.   +12 more
core   +4 more sources

Reciprocal control of viral infection and phosphoinositide dynamics

FEBS Letters, EarlyView.
Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley   +1 more source

Learning causal abstractions of linear structural causal models

UAI '24: Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence
The need for modelling causal knowledge at different levels of granularity arises in several settings. Causal Abstraction provides a framework for formalizing this problem by relating two Structural Causal Models at different levels of detail. Despite increasing interest in applying causal abstraction, e.g.
Massidda, Riccardo, Magliacane, Sara, Bacciu, Davide   +2 more
openaire   +4 more sources

Discovering causal models for structural, construction and defense-related engineering phenomena

Defence Technology
Causality, the science of cause and effect, has made it possible to create a new family of models. Such models are often referred to as causal models. Unlike those of mathematical, numerical, empirical, or machine learning (ML) nature, causal models hope
M.Z. Naser
doaj   +1 more source

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders [PDF]

, 2018
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities.
Forré, Patrick, Mooij, Joris M.
core   +1 more source