The perils of policy by p-value: Predicting civil conflicts

By Michael D. Ward, Brian Greenhill, Kristin M. Bakke. 2010

489 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/257cb4317665951b77aa52d4510c31c06c11de87

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Political Science and International Relations

1. Hugh Ward
2. David S. Siroky
3. Tobias Böhmelt
4. Sayash Kapoor
5. Arvind Narayanan
6. E. Chenoweth
7. Jay Ulfelder

In Sociology and Political Science

1. David Muchlinski
2. Brilé Anderson
3. Hugh Ward
4. David S. Siroky
5. Tobias Böhmelt
6. Sayash Kapoor
7. Arvind Narayanan
8. E. Chenoweth

Citations in the corpus

• Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data (2016, background): “However, even highly credible causal research designs will often account for small fractions of the overall variation in the outcome; in this sense, even the most rigorously identified causes may not be particularly strong predictors (Ward et al. 2010).”
• Can Structural Conditions Explain the Onset of Nonviolent Uprisings? (2017, methodology): “To assess and compare the explanatory power of these prominent models, we adopted an approach consistent with the scientific principle that a theory’s predictive power is often the most informative test of its ‘‘truth’’ (Hill and Jones 2014; Schrodt 2010; Ward et al. 2013). As Beck, King, and Zeng (”
• Public opinion and environmental policy output: a cross-national analysis of energy policies in Europe (2017, background): “We also assess the ability of our core explanatory variable to predict renewable energy policies (see Ward et al 2010) and evaluate the robustness of our main finding by considering alternative hypotheses pertaining to media attention and Internet search-term intensity (‘climate change’) in the…”
• Demand, supply, and restraint: Determinants of domestic water conflict and cooperation (2014, unknown): “Generally, models with more predictive power generate ‘‘true positives at the expense of fewer false positives’’ (Ward et al., 2010, p. 366).”
• Leakage and the reproducibility crisis in machine-learning-based science (2023, methodology): “This is despite the fact that the LR models were not trained to optimize predictive accuracy: they were conceived as explanatory models to understand past conflicts instead of predicting future ones.(47,81,82) We test our model info sheets on the four civil war prediction papers with errors and find”