78 citations · 138 across the 9 of their papers we have counts for
12 papers
Machine Learning Models Trained on a Longitudinal Survey Dataset Poorly Predict Hundreds of Life Outcomes
Emily M. Cantrell, Pranay Anchuri, Hanzhang Ren +1
Understanding the predictability of life outcomes is important for public policy and science, but existing studies focus on a limited number of outcomes and therefore struggle to d…
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
The Book of Life approach: Enabling richness and scale for life course research
Mark D. Verhagen, Benedikt Stroebl, Tiffany Liu +2
For over a century, life course researchers have faced a choice between two dominant methodological approaches: qualitative methods that analyze rich data but are constrained to sm…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
The origins of unpredictability in life trajectory prediction tasks
Ian Lundberg, Rachel Brown-Weinstock, Susan Clampet-Lundquist +5
Why are life trajectories difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multi-decade longitudinal stud…
REFORMS: Reporting Standards for Machine Learning Based Science
Sayash Kapoor, Emily Cantrell, Kenny Peng +16
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and ge…
Privacy, ethics, and data access: A case study of the Fragile Families Challenge
Ian Lundberg, Arvind Narayanan, Karen Levy +1
Stewards of social science data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. A…
The network survival method for estimating adult mortality: Evidence from a survey experiment in Rwanda
Dennis M. Feehan, Mary Mahy, Matthew J. Salganik
Adult death rates are a critical indicator of population health and wellbeing. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastr…
Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations
Dennis M. Feehan, Matthew J. Salganik
The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic sca…
Diagnostics for Respondent-driven Sampling
Krista J. Gile, Lisa G. Johnston, Matthew J. Salganik
Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially groups most at-risk for HIV/AIDS. Data are collected through…
Wiki surveys: Open and quantifiable social data collection
Matthew J. Salganik, Karen E. C. Levy
In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advance…