Respuesta :
The development of models that capture causal linkages has been slow.
Causal models:
- The development of richer models with neural topologies, implicit densities, and scalable algorithms for their Bayesian inference has accelerated in probabilistic generative models. Models that capture causal links, such as how certain genetic variables contribute to important human diseases, have made very little progress. In this study, we particularly pay attention to two difficulties: How can we create more complex causal models that can account for complex interconnections and highly nonlinear correlations between different causes. How can we account for latent confounders—variables that affect both cause and effect and hinder the discovery of causal relationships—in our analyses.
- We use concepts from causality and contemporary probabilistic modeling to address these problems. For the first, we discuss implicit causal models, a category of causal models that makes use of implicitly dense neural networks. For the second, we outline an implicit causal model that shares strength across examples to account for confounders. We scale Bayesian inference in experiments on up to a billion genetic measurements. We successfully identify causal factors with state-of-the-art accuracy, outperforming conventional genetics approaches by an absolute difference of 15 to 45.3%.
Learn more about causal models here brainly.com/question/13500892
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