Social scientists are now in an era of data abundance, and machine learning tools are
increasingly used to extract meaning from data sets both massive and small. We explain …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

There is intense interest in applying machine learning to problems of causal inference in
fields such as healthcare, economics and education. In particular, individual-level causal …

Multiplicative interaction models are widely used in social science to examine whether the
relationship between an outcome and an independent variable changes with a moderating …

This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …

We discuss models and data of crowd disasters, crime, terrorism, war and disease
spreading to show that conventional recipes, such as deterrence strategies, are often not …

A fully revised edition of the classic reference on concepts and their role in social science
research Social Science Concepts and Measurement offers an updated look at the theory …

The most commonly used statistical models of civil war onset fail to correctly predict most
occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of …

Despite the prevalence of nonviolent uprisings in recent history, no existing scholarship has
produced a generalized explanation of when and where such uprisings are most likely to …

We study rare events data, binary dependent variables with dozens to thousands of times
fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological …