# What can we Learn from Predictive Modeling?

arXiv: https://arxiv.org/abs/1612.05844 · HTML: https://mlsanity.com/paper/1612.05844/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2016.

## Built on by (most cited first, 6 of 6)

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- [Stance Detection: A Practical Guide to Classifying Political Beliefs in Text](https://mlsanity.com/paper/2305.01723/) (2023)
- [From Causal Discovery to Dynamic Causal Inference in Neural Time Series](https://mlsanity.com/paper/2603.20980/) (2026)
- [Dynamic Attention (DynAttn): Interpretable High-Dimensional Spatio-Temporal Forecasting (with Application to Conflict Fatalities)](https://mlsanity.com/paper/2512.21435/) (2025)
- [Towards Public Administration Research Based on Interpretable Machine Learning](https://mlsanity.com/paper/2601.06205/) (2026)
- [Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization](https://mlsanity.com/paper/2609.34115/) (2026)

## Abstract (arXiv)

The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model's parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.