A Pragmatist’s Guide to Using Prediction in the Social Sciences

Abstract

Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.

A data-driven approach shows that individuals' characteristics are more important than their networks in predicting fertility preferences

“…Recent research convincingly argues that data-driven methods, which are often focused on prediction, can advance research in the social sciences [38][39][40][41]. Prediction is sometimes seen as in opposition to understanding, but it is better conceived as a complementary form of understanding to the traditional theory-driven approach [39,42,43].…”

Statistical Modeling: The Three Cultures

“…While the distinction between predictive and causal statements has contributed to holding the truce among different quantitative research traditions (Freedman, 1991;Watts, 2014), unease is rising as scholars are increasingly using machine learning (ML) algorithms to analyze social phenomena (Bail, 2017;Lazer et al, 2020;Molina & Garip, 2019;Nelson, 2020;Shmueli, 2010;Turco & Zuckerman, 2017;Verhagen, 2022;Watts, 2017). ML is the study of how algorithms can learn from data (e.g., past social events) with no or little human guidance, thereby predicting new data instances (e.g., future social events) (Hastie et al, 2009).…”

“…Although many scholars agree that explaining social events requires causal statements, there is much less agreement on whether causal research also needs to be predictive (Boudon, 2005;Hedström & Ylikoski, 2010;Hofman et al, 2017;Keuschnigg et al, 2017;Marini & Singer, 1988;Merton, 1968;Shmueli, 2021;Verhagen, 2022). For example, Duncan Watts argues that "if [social scientists] want their explanations to be scientifically valid, they must evaluate them specifically on those grounds-in particular, by forcing them to make predictions" (2014, p. 313).…”

Accelerating progress in the social sciences: the potential of benchmarks

“…Although benchmarks are mostly focused on assessing predictions rather than causality, we argue, following Hofman et al (2017) and Verhagen (2022) that predictions and explanations should be treated as complements rather than substitutes. It is important to emphasize that using predictions to validate the explanatory power of models does not imply that complex predictive models should replace traditional approaches in the social sciences.…”

“…What is more, the incorporation of predictions in typical social science research can bring about several benefits. For instance, the use of predictions allows for a more detailed assessment of model fit and it provides a measure that allows researchers to compare the fit of highly different methodological approaches (Verhagen, 2022).…”