A survey of learning causality with data: Problems and methods
This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …
ability to learn causal effects and relations. In what ways is learning causality in the era of …
Data-driven causal effect estimation based on graphical causal modelling: A survey
In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …
causal effects from non-experimental data is crucial for understanding the mechanism …
Deconfounding duration bias in watch-time prediction for video recommendation
Watch-time prediction remains to be a key factor in reinforcing user engagement via video
recommendations. It has become increasingly important given the ever-growing popularity …
recommendations. It has become increasingly important given the ever-growing popularity …
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
Discovering causal relationships from observational data is a crucial problem and it has
applications in many research areas. The PC algorithm is the state-of-the-art constraint …
applications in many research areas. The PC algorithm is the state-of-the-art constraint …
High-dimensional consistency in score-based and hybrid structure learning
Main approaches for learning Bayesian networks can be classified as constraint-based,
score-based or hybrid methods. Although high-dimensional consistency results are …
score-based or hybrid methods. Although high-dimensional consistency results are …
[PDF][PDF] PC algorithm for nonparanormal graphical models.
The PC algorithm uses conditional independence tests for model selection in graphical
modeling with acyclic directed graphs. In Gaussian models, tests of conditional …
modeling with acyclic directed graphs. In Gaussian models, tests of conditional …
On efficient adjustment in causal graphs
We consider estimation of a total causal effect from observational data via covariate
adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting …
adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting …
A novel method to detect functional microRNA regulatory modules by bicliques merging
MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their
targets. They are known to work cooperatively with genes and play important roles in …
targets. They are known to work cooperatively with genes and play important roles in …
Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems
Background Learning the causal structure helps identify risk factors, disease mechanisms,
and candidate therapeutics for complex diseases. However, although complex biological …
and candidate therapeutics for complex diseases. However, although complex biological …
What are the relevant sources and factors affecting event mean concentrations (EMCs) of nutrients and sediment in stormwater?
Urbanization increases runoff, sediment, and nutrient loadings downstream, causing
flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) …
flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) …