StatsPAI is a validation-tiered Python library for causal inference and applied econometrics. One import, 1,000+ registered functions across 80+ submodules (live count: python ...
Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machine learning framework to real-world data from the ...
I built a complete, end-to-end Causal Machine Learning pipeline for Treatment Effect Estimation — one of the most important and underappreciated problems in applied data science. Here's everything ...
Celcomen leverages a mathematical causality framework to disentangle intra- and inter-cellular gene regulation programs in spatial transcriptomics data through a generative graph neural network. It is ...
Causal inference is one of the most important and challenging aims in statistics and data science. Many fields, from clinical medicine to social sciences, strive to use empirical data to understand ...
Growing evidence has indicated that the nutritional quality of dietary intake and alterations in blood metabolites were related to human brain activity. This study aims to investigate the causal ...
dDepartment of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA eDepartment of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii ...
Confocal imaging maps the intracellular distribution of Cy5-labeled polyplexes, providing estimates of the proportion of pDNA partitioned between the cytoplasmic and nuclear regions. The lead polymer ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results