Causal inference
Causal inference is a field of study that focuses on identifying and establishing cause-and-effect relationships between variables.
statistics
Michael I. Jordan
Michael I. Jordan is a prominent figure in the fields of artificial intelligence and machine learning, known for his significant contributions to statistical modeling and algorithms.
Multidimensional scaling (MDS)
Multidimensional scaling (MDS) is a statistical technique used for visualizing the level of similarity of individual cases in a dataset.
Canonical correlation analysis (CCA)
Canonical correlation analysis (CCA) is a statistical method used to understand the relationships between two multivariate datasets. It identifies linear combinations of variables that are maximally correlated.
Fully parameterized quantile function (FQF)
The Fully Parameterized Quantile Function (FQF) is a statistical approach used to estimate quantiles across various distributions, enhancing flexibility in modeling.
Causal graph
A causal graph is a visual representation of causal relationships among variables, often used in statistics and data analysis.
Logistic regression
Logistic regression is a statistical method for predicting binary classes. It models the probability of a certain class or event occurring.
Logit lens
The Logit lens is a statistical tool used in various fields to model and analyze categorical outcomes. It is particularly significant in fields like economics, healthcare, and social sciences.
Principal component analysis (PCA)
Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of data by transforming it into a new set of variables called principal components. These components capture the maximum variance within the data, enabling easier visualization, interpretation, and noise reduction.
Linear discriminant analysis (LDA)
Linear Discriminant Analysis (LDA) is a statistical method for classifying data by finding a linear combination of features that best separates two or more classes.