62 citations · 87 across the 4 of their papers we have counts for
6 papers
Learning Optimal Interventions
Jonas Mueller, David N. Reshef, George Du +1
Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each…
An Empirical Study of Leading Measures of Dependence
David N. Reshef, Yakir A. Reshef, Pardis C. Sabeti +1
In exploratory data analysis, we are often interested in identifying promising pairwise associations for further analysis while filtering out weaker, less interesting ones. This ca…
Equitability, interval estimation, and statistical power
Yakir A. Reshef, David N. Reshef, Pardis C. Sabeti +1
For analysis of a high-dimensional dataset, a common approach is to test a null hypothesis of statistical independence on all variable pairs using a non-parametric measure of depen…
Measuring dependence powerfully and equitably
Yakir A. Reshef, David N. Reshef, Hilary K. Finucane +2
Given a high-dimensional data set we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and r…
Theoretical Foundations of Equitability and the Maximal Information Coefficient
Yakir A. Reshef, David N. Reshef, Pardis C. Sabeti +1
The maximal information coefficient (MIC) is a tool for finding the strongest pairwise relationships in a data set with many variables (Reshef et al., 2011). MIC is useful because…
Equitability Analysis of the Maximal Information Coefficient, with Comparisons
David Reshef, Yakir Reshef, Michael Mitzenmacher +1
A measure of dependence is said to be equitable if it gives similar scores to equally noisy relationships of different types. Equitability is important in data exploration when the…