Conducting political science research with observational data presents challenges when full replication isn't possible due to the absence of controlled stochastic mechanisms.

Addressing Research Challenges

This article argues that scholars can enhance evidence for predictive validity by estimating and minimizing generalization error. Unlike experimental settings, exact replication here is often unattainable while reproduction remains feasible.

Evidence Through Minimization

By assessing how well their models predict across different scenarios or populations (estimating generalization error) and then adjusting those models to reduce this estimate (a process called regularization), researchers provide evidence about the applicability of their findings beyond what they've directly observed.

Improving Model Accuracy

This method helps identify potential overfitting issues, where a model captures study-specific patterns but lacks broader predictive power. It also allows scholars to compare models by highlighting systematic features that might be missing from others, thus preventing underfitting.

Practical Significance

Minimizing generalization error offers a way to quantify the expected reliability of predictions in observational studies, thereby strengthening their external validity and boosting confidence in study conclusions.