Predictive Analytics for Improving Services
Overview
Defining PA for improving services
Scoping a PA project
Conducting a PA proof-of-concept
- The importance of a proof-of-concept
- Defining learners
- Specifying a benchmark learner
- Adding and comparing learners
Introduction to the PA Toolset
- Overview
- How to access toolset
- How to specify scope in toolset
- How to specify learners in toolset
Data for PA: PART 1
Data for PA: PART 2
- Oveview of data quality
- Handling missing data
- Stability of measures over time
- Data leakage
- Strategies for extracting predictive value
- Data model for PA Toolset
Modeling approaches
- Regression for predicting binary outcomes
- Machine learning (ML) algorithms for predicting binary outcomes
Evaluating and comparing predictive performance
- Prediction output from learner training
- Metrics for evaluating predictive performance
- Visualiziations of predictive performance
- Guidance for selecting metrics
Evaluating and comparing bias
- Sources of bias
- Metrics for assessing bias
- Visualizations for assessing bias
- Guidance for selecting the metrics
- Addressing bias
Selecting and validating a learner
- Considerations for selecting a learner
- Validating a learner
Understanding and communicating results
- Results at the individual level
- Results at the aggregate level
- What can we say about which predictors matter most?
- Reviewing limitations and ethical considerations
Deploying predictive analytics
- Deciding whether PA should be deployed
- Approaches for deployment
- Monitoring performance and fairness over time
Additional optional material
- Predictive Analytics vs. Inferential Statistics

Who are we making predictions for?

Our target population is specifically adult TANF recipients who are eligible and approved for the TANF department’s education and training program and who are required to work.

Our data sets include a broader group of TANF applicants and participants so we need to be careful to exclude those who do not meet our criteria.