Robust Counterfactual Inferences using Feature Learning and their Applications

Working with a new data visualization tool, and wondering how to create a specific type of chart? The Chartmaker Directory (designed by Andy Kirk) indexes more than 35 tools and over 50 charts, and provides links to examples from each combination. Here’s an intentionally small detail from the index, where each hollow dot in a column represents a sample chart created a tool (the rightmost column is R, for example), and solid dots also include code. 

To quickly recap, so far we’ve just worked with some single values to get to grips with how some of the various operations work. Of course, we rarely work with a single value! If we did, we could just use a calculator.

Estimation of Marginal Treatment Effects using Local Instrumental Variables (localIV)In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such …

Using a mathematical model based on satellite data, NASA shows an estimate of aerosol in the atmosphere on August 23, 2018:

Adversarially Learned Mixture Model (AMM) The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering. The AMM is the first adversarially optimized method to model the conditional dependence between inferred continuous and categorical latent variables. Experiments on the MNIST and SVHN datasets show that the AMM allows for semantic separation of complex data when little or no labeled data is available. The AMM achieves a state-of-the-art unsupervised clustering error rate of 2.86% on the MNIST dataset. A semi-supervised extension of the AMM yields competitive results on the SVHN dataset. …

Roger Peng ** 2018/08/24

In this article, we will try to understand the concept behind box plots. When i first saw a box plot, I was utterly confused and could not extract much information out of it on the first go. This article will help you to avoid the situation I faced in understanding a box plot.

John Williams points us to this article, “Designing river flows to improve food security futures in the Lower Mekong Basin,” by John Sabo et al., featured in the journal Science. Williams writes:

This note shares an experiment comparing the performance of a number of data processing systems available in R. Our notional or example problem is finding the top ranking item per group (group defined by three string columns, and order defined by a single numeric column). This is a common and often needed task.