Potpourri: Statistics #67

726. Computational Causal Inference at Netflix727. Tools for Ex-post Survey Data Harmonization728. How to pick more beautiful colors for your data visualizations729. Shiny in Production: App and Database Syncing730. Introduction to Causal Inference731. An Illustration of Decision Trees and Random Forests with an Application to the 2016 Trump Vote732. Key things to know about election polling in the United States733. State-of-the-art NLP models from R734. Introduction to Stan in R735. How to write your own R package and publish it on CRAN736. Bayesian Analysis for A/B Testing737. Estimating House Effects738. Heatmaps in ggplot2739. The Taboo Against Explicit Causal Inference in Nonexperimental Psychology740. Spreadsheet workflows in R741. A beginner's guide to Shiny modules742. Dataviz Interview743. 10 Things to Know About Survey Experiments744. Applying Weights745. Creating effective interrupted time series graphs: Review and recommendations746. Lasso and the Methods of Causality747. How We Designed The Look Of Our 2020 Forecast748. Taking Control of Plot Scaling749. ~~How to measure spatial diversity and segregation?~~750. 10+ Guidelines for Better Tables in R751. How maps in the media make us more negative about migrants752. Comparing two proportions in the same survey753. Quantitative Social Science Methods, I (Gov2001 at Harvard University)754. Introduction to Computational Thinking755. Creating R Packages with devtools756. Introduction to Statistical Learning in R757. Textrecipes series: Pretrained Word Embedding