Results 111 to 120 of about 917,002 (254)

From urn models to box models: Making Neyman's (1923) insights accessible

Journal of Causal Inference
Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston, Dudoit Sandrine, Nolan Deborah, Speed Terence P.   +3 more
doaj   +1 more source

Invariant Causal Prediction for Nonlinear Models

Journal of Causal Inference, 2018
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure.
Heinze-Deml Christina, Peters Jonas, Meinshausen Nicolai   +2 more
doaj   +1 more source

Causal Markov, Robustness and the Quantum Correlations [PDF]

, 2009
It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue ...
San Pedro, Iñaki, Suárez, Mauricio, Mauricio Suárez, Iñaki San Pedro   +3 more
core   +1 more source

PAK1 activation drives divergent resistance mechanisms to aromatase inhibition and tamoxifen in a luminal: A breast cancer model

Molecular Oncology, EarlyView.
Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller, Janina Müller, Efstathios‐Iason Vlachavas, Lukas Beumers, Lena Fleischhacker, Sara Burmester, Angelika Wörner, Sabine Karolus, Dominic Helm, Cindy Körner, Stefan Wiemann   +10 more
wiley   +1 more source

Conditional generative adversarial networks for individualized causal mediation analysis

Journal of Causal Inference
Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine.
Huan Cheng, Sun Rongqian, Song Xinyuan
doaj   +1 more source

Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

Journal of Causal Inference, 2019
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many ...
Strobl Eric V., Zhang Kun, Visweswaran Shyam   +2 more
doaj   +1 more source

In vitro and in silico modelling of ROS1‐positive non‐small cell lung cancer reveals fusion‐dependent tyrosine kinase inhibitor responses

Molecular Oncology, EarlyView.
Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq, Marc Terrones, Felicia Rodrigues Fortes, Anne Schepers, Alessia Denis, Christophe Deben, Ken Op de Beeck, Guy Van Camp, Geert Vandeweyer   +8 more
wiley   +1 more source

Prospective and retrospective causal inferences based on the potential outcome framework

Journal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen, Wei Shaojie   +4 more
doaj   +1 more source

Causal inference for binary regression with observational data [PDF]

Special problems arise when trying to do causal inference for binary regression with observational data; we will examine some of these problems and critically examine several common and not-so-common solutions.
Austin Nichols
core  

The causal manipulation of chain event graphs [PDF]

, 2007
Discrete Bayesian Networks (BN’s) have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that ...
Riccomagno, Eva, Smith, J. Q.
core