enrolled in aca

WarningStaging

Built from the latest training run on 2026-09-30 17:45 UTC, not from a released model set. The released documentation is at https://matter.indigo.engineering/docs/.

Overview

This model predicts whether a voter is enrolled in a health insurance plan through the Affordable Care Act marketplace.

Trained on survey data, it draws on age, political engagement, household composition, and area-level demographics to estimate enrollment likelihood.

These scores help organizations identify ACA enrollees for outreach around open enrollment, coverage protections, and health care policy.

  • approach: boosted tree classifier
  • training rows: 5,373
  • last updated: 2026-09-06 17:47:01

Validation

Lift Chart

Each bar shows how much more common the outcome is within a score decile than in the population overall. Bars taller than the dashed line (lift of 1) mean the model’s top-ranked groups concentrate the outcome; a steady decline from decile 1 to 10 indicates the model is ranking effectively.

Calibration Plot

Bars of similar height within each decile indicate well-calibrated predictions. Systematic differences reveal where the model over- or under-predicts.

Cumulative Gains

The further the model curve bows above the diagonal, the better the model concentrates the outcome in its top-ranked predictions.

Decile Analysis Table

decile count mean actual mean predicted mae lift
10 138 0.20 0.19 0.01 2.99
9 137 0.11 0.09 0.02 1.67
8 137 0.04 0.07 0.03 0.67
7 138 0.12 0.06 0.06 1.88
6 137 0.06 0.06 0.002 0.89
5 137 0.03 0.05 0.02 0.45
4 138 0.06 0.04 0.01 0.89
3 137 0 0.04 0.04 0.00
2 137 0.007 0.03 0.03 0.11
1 138 0.03 0.03 0.003 0.44

Architecture

This model is a boosted-tree classifier trained with BigQuery ML.

Hyperparameter configuration

  • max iterations: 20
  • learn rate: 0.4667
  • l1 regularization: 1.913e-08
  • l2 regularization: 2e-04
  • min relative progress: 0.01
  • early stop: TRUE
  • input label columns: benefits_enrolled_in_aca
  • data split method: AUTO_SPLIT
  • max tree depth: 3
  • subsample: 0.7158
  • min split loss: 0
  • num trials: 12
  • max parallel trials: 4
  • hparam tuning objectives: ROC_AUC
  • enable global explain: TRUE
  • category encoding method: LABEL_ENCODING
  • booster type: GBTREE
  • num parallel tree: 1
  • tree method: AUTO
  • min tree child weight: 1
  • instance weight column: sample_weight
  • xgboost version: 0.9
  • auto class weights: FALSE

Features

Importance

Sourcing

Attom Property Data API

  • land market value
  • last sale price
  • lot size (acres)

CDC PLACES

  • tract % adults with a routine checkup

CDC WONDER

  • routine doctor checkup, past year (adults)

Georgetown University Center for Children and Families

  • county medicaid / chip coverage rate

State voter files (via TargetSmart)

  • age

TargetSmart

  • children present score
  • double negative democrat score
  • gun owner score
  • income rank
  • local election voter score
  • marriage score
  • mormon score
  • non-presidential primary turnout score
  • race: latino
  • race: native american

U.S. Census American Community Survey

  • block group % high school diploma
  • block group % income $150–200k
  • block group % income $50–100k

U.S. Census CPS Annual Social and Economic Supplement

  • current medicare coverage
  • dwelling ownership

US Department of Labor

  • county median weekly childcare cost, preschool

Implementation

Prediction distribution

statistic value
mean 0.05
std dev 0.03
min 0.02
25th 0.03
median 0.04
75th 0.05
max 1

Segments

segment lowest score highest score share of records
Most likely 0.09 1 4.4%
Somewhat likely 0.07 0.08 4.1%
Least likely 0.02 0.06 91.5%

External validity

Our model indicates that 5% of people with scored records are enrolled in an ACA marketplace health plan. CMS 2024 Marketplace Open Enrollment Report puts marketplace enrollment at roughly 7% of U.S. adults.