Skip to main navigation
Skip to search
Skip to main content
WashU Medicine Research Profiles Home
Help & FAQ
Home
Profiles
Departments, Divisions and Centers
Research output
Search by expertise, name or affiliation
USING COUNTERFACTUAL MODELING AND MACHINE LEARNING GENERATED PROPENSITY SCORES TO EXAMINE BLACK SOCIAL CONTROL AND MATHEMATICS
Odis Johnson
, Jason Jabbari
Institute of Clinical and Translational Sciences (ICTS)
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapter
›
peer-review
2
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'USING COUNTERFACTUAL MODELING AND MACHINE LEARNING GENERATED PROPENSITY SCORES TO EXAMINE BLACK SOCIAL CONTROL AND MATHEMATICS'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Propensity Score
100%
Machine Learning
100%
Modelling Learning
100%
Social Control
100%
Counterfactual Modeling
100%
Racial Disparities
25%
Causal Inference
25%
Justice
25%
Policy Making
25%
Strong Inference
25%
National Priorities
25%
Black Students
25%
Mathematics Performance
25%
Minoritized Students
25%
School-to-prison pipeline
25%
College Attendance
25%
STEM pipeline
25%
State Violence
25%
High School Longitudinal Study
25%
Exclusionary Discipline
25%
Social Sciences
Propensity Score
100%
Science, Technology, Engineering and Mathematics
100%
Social Control
100%
Longitudinal Analysis
50%
Secondary Schools
25%
Justice
25%
Mathematics Performance
25%
Inequity
25%
Causal Inference
25%
Psychology
Social Control
100%
Longitudinal Analysis
50%
Causal Inference
25%
Mathematics Performance
25%