1. The latest *Chronicle of Higher Education* includes **a piece on forecasting international affairs** (here) by Beth McMurtrie, who asserts that

Forecasting is undergoing a revolution, driven by digitized data, government money, new ways to analyze information, and discoveries about how to get the best forecasts out of people.

The article covers terrain that is familiar to anyone working in this field, but I think it gives a solid overview of the current landscape. (Disclosure: I’m quoted in the piece, and it describes several research projects for which I have done or now do paid work.)

2. Yesterday, I discovered **a new R package that looks to be very useful for evaluating and comparing forecasts**. It’s called ‘scoring‘, and it does just that, providing functions to implement an array of proper scoring rules for probabilistic predictions of binary and categorical outcomes. The rules themselves are nicely discussed in a 2013 publication co-authored by the package’s creator, Ed Merkle, and Mark Steyvers. Those rules and a number of others are also discussed in a paper by Patrick Brandt, John Freeman, and Phil Schrodt that appeared in the *International Journal of Forecasting* last year (earlier ungated version here).

I found the package because I was trying to break the habit of always using the area under the ROC curve, or AUC score, to evaluate and compare the accuracy of forecasts from statistical models of rare events. AUC is quite useful as far as it goes, but it doesn’t address all aspects of forecast accuracy we might care about. Mathematically, the AUC score represents the probability that a prediction selected at random from the set of cases that had an event of interest (e.g., a coup attempt or civil-war onset) will be larger than a prediction selected at random from the set of cases that didn’t. In other words, AUC deals strictly in relative ranking and tells us nothing about calibration.

This came up in my work this week when I tried to compare out-of-sample estimates from three machine-learning algorithms—kernel-based regularized least squares (KRLS), Random Forests (RF), and support vector machines (SVM)—trained on and then applied to the same variables and data. In five-fold cross-validation, the three algorithms produced similar AUC scores, but histograms of the out-of-sample estimates showed much less variance for KRLS than RF and SVM. The mean out-of-sample “forecast” from all three was about 0.009, the base rate for the event, but the maximum for KRLS was only about 0.01, compared with maxes in the 0.4s and 0.7s for the others. It turned out that KRLS was doing about as well at rank ordering the cases as RF and SVM, but it was much more conservative in estimating the likelihood of an event. To consider that difference in my comparisons, I needed to apply scoring rules that were sensitive to forecast calibration and my particular concern with avoiding false negatives, and Merkle’s ‘scoring’ package gave me the functions I needed to do that. (More on the results some other time.)

3. Last week, Andreas Beger wrote a great post for the WardLab blog, Predictive Heuristics, cogently explaining **why event data is so important to improving forecasts of political crises**:

To predict something that changes…you need predictors that change.

That sounds obvious, and in one sense it is. As Beger describes, though, most of the models political scientists have built so far have used slow-changing country-year data to try to anticipate not just where but also when crises like coup attempts or civil-war onsets will occur. Some of those models are very good at the “where” part, but, unsurprisingly, none of them does so hot on the “when” part. Beger explains why that’s true and how new data on political events can help us fix that.

4. Finally, Chris Blattman, Rob Blair, and Alexandra Hartman have posted a new working paper on **predicting violence at the local level in “fragile” states**. As they describe in their abstract,

We use forecasting models and new data from 242 Liberian communities to show that it is to possible to predict outbreaks of local violence with high sensitivity and moderate accuracy, even with limited data. We train our models to predict communal and criminal violence in 2010 using risk factors measured in 2008. We compare predictions to actual violence in 2012 and find that up to 88% of all violence is correctly predicted. True positives come at the cost of many false positives, giving overall accuracy between 33% and 50%.

The patterns Blattman and Blair describe in that last sentence are related to what Beger was talking about with cross-national forecasting. Blattman, Blair, and Hartman’s models run on survey data and some other structural measures describing conditions in a sample of Liberian localities. Their predictive algorithms were derived from a single time step: inputs from 2008 and observations of violence from 2010. When those algorithms are applied to data from 2010 to predict violence in 2012, they do okay—not great, but “[similar] to some of the earliest prediction efforts at the cross-national level.” As the authors say, to do much better at this task, we’re going to need more and more dynamic data covering a wider range of cases.

Whatever the results, I think it’s great that the authors are trying to forecast at all. Even better, they make explicit the connections they see between theory building, data collection, data exploration, and prediction. On that subject, the authors get the last word:

However important deductive hypothesis testing remains, there is much to gain from inductive, data-driven approaches as well. Conflict is a complex phenomenon with many potential risk factors, and it is rarely possible to adjudicate between them on ex ante theoretical grounds. As datasets on local violence proliferate, it may be more fruitful to (on occasion) let the data decide. Agnosticism may help focus attention on the dependent variable and illuminate substantively and statistically significant relationships that the analyst would not have otherwise detected. This does not mean running “kitchen sink” regressions, but rather seeking models that produce consistent, interpretable results in high dimensions and (at the same time) improve predictive power. Unexpected correlations, if robust, provide puzzles and stylized facts for future theories to explain, and thus generate important new avenues of research. Forecasting can be an important tool in inductive theory-building in an area as poorly understood as local violence.

Finally, testing the predictive power of exogenous, statistically significant causes of violence can tell us much about their substantive significance—a quantity too often ignored in the comparative politics and international relations literature. A causal model that cannot generate predictions with some reasonable degree of accuracy is not in fact a causal model at all.