Results 91 to 100 of about 917,002 (254)
y0-causal-inference/y0: v0.2.6
<h2>What's Changed</h2> <ul> <li>Improve variable sort and CF graph testing by @cthoyt in https://github.com/y0-causal-inference/y0/pull/199</li> <li>Use frozensets for interventions by @cthoyt in https://github.com/y0-
Jeremy Zucker +4 more
core +1 more source
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
Generalizing Experimental Findings
Journal of Causal Inference, 2015 This note examines one of the most crucial questions in causal inference: “How generalizable are randomized clinical trials?” The question has received a formal treatment recently, using a non-parametric setting, and has led to a simple and general ...
Pearl Judea
doaj +1 more source
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
Journal of Causal Inference, 2017 1 In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes.
Zheng Wenjing, van der Laan Mark
doaj +1 more source
y0-causal-inference/y0: v0.2.7
<h2>What's Changed</h2> <ul> <li>Add SCM parameter estimation by @cthoyt in https://github.com/y0-causal-inference/y0/pull/201</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/y0 ...
Jeremy Zucker +4 more
core +1 more source
Models, identifiability, and estimability in causal inference
, 2023 Here we discuss two common but, in our view, misguided assumptions in causal inference. The first assumption is that one requires potential outcomes, directed acyclic graphs (DAGs), or structural causal models (SCMs) for thinking about causal ...
Maclaren, Oliver John, Nicholson, Ruanui
core
An epithelial GPR35 isoform supports tumor‐associated transcriptional and metabolic phenotypes
FEBS Letters, EarlyView.GPR35 generates two functionally distinct isoforms with previously unresolved roles. GPR35‐short mediates immune‐cell chemotaxis, while GPR35‐long is enriched in colorectal cancer epithelium, where it supports increased metabolism, proliferation, and tumor‐associated transcriptional programs.
Jørgen D. Rønneberg +14 more
wiley +1 more source
, 2010 A variety of questions in causal inference can be represented as probability distributions over hypothetical worlds where idealized randomized experiments known as interventions have taken place.
Tian, Jin, Shpitser, Ilya
core +1 more source
Data-Adaptive Causal Effects and Superefficiency
Journal of Causal Inference, 2016 Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency.
Aronow Peter M.
doaj +1 more source