The large majority of inferences drawn in empirical political research follow from model-
based associations (eg, regression). Here, we articulate the benefits of predictive modeling …

Recent advances in causal inference have given rise to a general and easy-to-use formula
for assessing the extent to which the effect of one variable on another is mediated by a third …

The do-calculus was developed in 1995 to facilitate the identification of causal effects in non-
parametric models. The completeness proofs of [Huang and Valtorta, 2006] and [Shpitser …

An accessible, contemporary introduction to the methods for determining cause and effect in
the Social Sciences “Causation versus correlation has been the basis of arguments …

The lack of theories and methodological weakness have been pointed out as two distinct but
related problems in empirical management information systems (MIS) research. Reinforcing …

Supplement to “Automated versus Do-It-Yourself Methods for Causal Inference: Lessons
Learned from a Data Analysis Competition”. The online supplement contains the full set of …

Longitudinal data are becoming increasingly common in social science research. In this
chapter, we discuss methods for exploiting the features of longitudinal data to study causal …

Nearly all hierarchical linear models presented to political science audiences are estimated
using maximum likelihood under a repeated sampling interpretation of the results of …

We develop an approach to multimethod research that generates joint learning from
quantitative and qualitative evidence. The framework—Bayesian integration of quantitative …