Results 11 to 20 of about 917,002 (254)

Assimilative causal inference

Nature Communications
Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
Marios Andreou, Nan Chen, Erik Bollt
doaj   +6 more sources

Variational Causal Inference

CoRR, 2022
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Wu, Yulun, Price, Layne C., Wang, Zichen, Ioannidis, Vassilis N., Barton, Robert A., Karypis, George   +5 more
openaire   +3 more sources

The Future of Causal Inference

American Journal of Epidemiology, 2022
Abstract The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal ...
Nandita, Mitra, Jason, Roy, Dylan, Small
openaire   +2 more sources

A Survey on Causal Inference

ACM Transactions on Knowledge Discovery from Data, 2021
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with ...
Liuyi Yao, Zhixuan Chu, Sheng Li 0001, Yaliang Li, Jing Gao 0004, Aidong Zhang 0001   +5 more
openaire   +2 more sources

Deep Learning for Causal Inference

, 2023
This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on building and optimizing custom deep learning models and shows how to adapt ...
Tim Sainburg, Bernard Koch, JIANG, SONG, Yizhou Sun, Pablo Geraldo, Jacob G. Foster   +5 more
core   +1 more source

y0-causal-inference/y0 v0.0.3

, 2021
y0 (pronounced "why not?") is for causal inference in ...
Jeremy Zucker, Marc-Antoine Parent, Charles Tapley Hoyt   +2 more
core   +1 more source

y0-causal-inference/y0: Implementation of ID* and IDC*

, 2023
After a very long (almost two year) road, we have implemented a complete ID and IDC algorithm. This is based on Shpitser and Pearl, 2012, however it fixes several issues with the original algorithm returning incorrect results.
Jeremy Zucker, Marc-Antoine Parent, Charles Tapley Hoyt   +2 more
core   +1 more source

sheaduarte/Multisensory-Causal-Inference-VR:

, 2022
Multisensory causal inference VR task made in Unity for Vive Pro Eye ...
Shea E. Duarte
core   +1 more source

Causal network inference using biochemical kinetics [PDF]

, 2014
Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models.
Bayani, Nora, Mukherjee, Sach, Dondelinger, Frank, Bayani, N, Korkola, J, Dondelinger, F, Mukherjee, S, Oates, Chris J., Gray, JW, Korkola, James, Gray, Joe W., Oates, CJ   +11 more
core   +1 more source

y0-causal-inference/y0: v0.2.5

, 2023
<h2>What's Changed</h2> <ul> <li>Update falsification by @cthoyt in https://github.com/y0-causal-inference/y0/pull/196</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/y0-causal ...
Jeremy Zucker, pnavada, Benjamin M. Gyori, Marc-Antoine Parent, Charles Tapley Hoyt   +4 more
core   +1 more source