Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration
Proceedings of the National Academy of Sciences 117(15): 8398-8403, 2020.

Abstract
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.
Related Publications
- [Paper] Short-Term Exposure to "Filter-Bubble" Recommendation Systems Has Limited Polarization Effects: Naturalistic Experiments on YouTube
- [Paper] REFORMS: Consensus-Based Recommendations for Machine-Learning-Based Science
- [Paper] Machine Learning for Social Science: An Agnostic Approach
- [Paper] Wrestling with Complexity in Computational Social Science: Theory, Estimation, and Representation
- [Conference Proceedings] How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
- [Conference Proceedings] EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footage
- [Working Paper] Where's the Evidence that Respondents Understand Your Survey Questions?
- [Paper] Handle with Care: A Sociologist's Guide to Causal Inference with Instrumental Variables