Related Experiment Video
Updated: Jul 31, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement
Avni M Shah1,2, Matthew Osborne2,3,4, Jacyln Lefkowitz Kalter5
1Department of Management, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada.
Abstract:
Individuals regularly struggle to save for retirement. Using a large-scale field experiment ( ) in Mexico, we test the effectiveness of several behavioral interventions relative to existing policy and each other geared toward improving voluntary retirement savings contributions. We find that an intervention framing savings as a way to secure one's family future significantly improves contribution rates. We leverage recursive partitioning techniques and identify that the overall positive treatment effect masks subpopulations where the treatment is even more effective and other groups where the treatment has a significant negative effect, decreasing contribution rates. Accounting for this variation is significant for theoretical and policy development as well as firm profitability. Our work also provides a methodological framework for how to better design, scale, and deploy behavioral interventions to maximize their effectiveness.
More Related Videos
Related Concept Videos
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Applications of Life Tables
Longitudinal Studies
Regression Toward the Mean
Mechanistic Models: Compartment Models in Individual and Population Analysis
Cross-Sectional Research

