Evidence from Causal Forest Double Machine Learning on 42 Million HMDA Applications, 2020–2024
Warning
Correction (October 2026). This repository is archived, and its headline claims should not be cited. The manuscript was rejected by the Journal of Financial Services Research in August 2026. Checked against the CFPB's public LAR field definitions, the referee's two data objections are correct:
- Public HMDA does not report a credit-score range.
applicant_credit_score_type(codes 1–8) records which scoring model was used, not the applicant's score. The analysis below treats it as an ordinal credit-score control, which it is not. - Public HMDA has no manual-underwriting indicator. AUS code 6 means "not applicable", 7 "internal proprietary system", and 1111 "exempt". The manual-versus-automated contrast reported below therefore does not measure human discretion.
The code is kept for transparency. The supported analysis of the same data is in hmda-racial-disparities.
Distribution of individual conditional average treatment effects — 90.7% of Black applicants face a negative racial penalty
This repository contains the complete, reproducible codebase for a causal analysis of racial disparities in U.S. mortgage lending. Using Home Mortgage Disclosure Act (HMDA) administrative data from 2020–2024 — encompassing 42.3 million applications — I estimate the conditional causal effect of racial identity on mortgage approval probability after controlling for 33 creditworthiness features including debt-to-income ratio, loan-to-value ratio, income, loan purpose, and underwriting system type.
The central finding is stark: a Black applicant who is identical to a White applicant on every observable financial characteristic still faces a conditional approval penalty of 9.4 percentage points. This penalty is largest when loans pass through manual underwriting — where human judgment plays a larger role — rather than automated systems, a contrast of 8.6 pp that points toward discretion as the mechanism amplifying racial disparities.
| Statistic | Value |
|---|---|
| Conditional racial penalty (DML, pooled) | −9.39 pp (SE = 0.071; t = −131.8) |
| Unconditional racial approval gap | −14.95 pp |
| Share of gap unexplained by 33 creditworthiness features | 62.8% |
| CATE standard deviation | 8.47 pp |
| Fraction of Black applicants penalised (CATE < 0) | 90.7% |
| Manual vs. Automated AUS contrast | −8.62 pp |
| DR-Learner replication (500K subsample) | −9.24 pp (Δ = 0.15 pp) |
| Race-shuffle placebo signal ratio | 17.9× |
| Oster (2019) δ at recommended R²_max | 6.87 |
| Cinelli-Hazlett (2020) RV₀ | 0.00512 |
| Total observations | 42,296,010 |
| Estimation sample | 1,500,000 (stratified) |
Black mortgage applicants in the U.S. face a conditional approval penalty of 9.4 percentage points relative to otherwise identical White applicants, after controlling for all available creditworthiness information. This penalty is largest for applicants processed through manual underwriting (−14.79 pp) versus automated systems (−6.17 pp) — a contrast of 8.62 pp — consistent with the hypothesis that human discretion amplifies racial disparities beyond what algorithmic systems alone produce.
- Framework: Partially Linear DML (Chernozhukov et al., 2018)
- Nuisance models: LightGBM gradient-boosted trees, 5-fold cross-fitting
- CATE estimation: CausalForestDML (Wager & Athey, 2018; Athey et al., 2019)
- Feature attribution: SHAP (Lundberg & Lee, 2017)
- RDD: Regression discontinuity at LTV = 80% PMI threshold → 1.81 pp discontinuity (t = 18.5)
- DiD: Difference-in-differences around 2022 Federal Reserve credit tightening → +0.99 pp widening
- DR-Learner replication: −9.24 pp ✓
- Race-shuffle placebo: 17.9× signal-to-noise ratio ✓
- Oster δ = 6.87 (unobservables must be 7× stronger than observables to nullify) ✓
- Cinelli-Hazlett RV₀ = 0.00512 (all observed covariates fall below threshold) ✓
Subgroup CATE Heterogeneity |
SHAP Attribution — AUS Type Dominates |
Annual DML Estimates, 2020–2024 |
Event Study — Gap Widened Post-2022 |
Robustness — All Estimators Converge |
Income × AUS Interaction Heatmap |
CATE-HMDA-Heterogeneous-Effects/
│
├── data/
│ ├── features_panel.parquet # 42.3M HMDA rows, 37 engineered features (not tracked — see data/README_data.md)
│ ├── cate_estimates.parquet # Individual CATEs for 1.5M estimation sample (not tracked)
│ ├── feature_sets.json # Feature set definitions (X_FULL, X_BASE)
│ ├── trim_bounds.json # Propensity score trim bounds [0.033, 0.580]
│ └── README_data.md # Data download and preprocessing instructions
│
├── notebooks/
│ ├── NB17_feature_engineering.ipynb # Feature construction (42M rows)
│ ├── NB18_overlap_diagnostics.ipynb # Propensity score & overlap diagnostics
│ ├── NB19_double_ml_baseline.ipynb # DML ATE estimation
│ ├── NB20_propensity_analysis.ipynb # Extended PS analysis
│ ├── NB21_causal_forest_cate.ipynb # CATE estimation & subgroup analysis
│ ├── NB22_shap_attribution.ipynb # SHAP feature decomposition
│ ├── NB23_disparity_map.ipynb # Personalised disparity mapping
│ ├── NB24_subgroup_rdd.ipynb # RDD analysis + 4 validity diagnostics
│ ├── NB25_subgroup_did.ipynb # DiD & event study
│ ├── NB26_robustness_checks.ipynb # DR-Learner + LinearDML robustness
│ ├── NB26_paper_figures.ipynb # Publication figure generation (earlier draft)
│ ├── NB27_sensitivity_analysis.ipynb # Oster & Cinelli-Hazlett bounds
│ └── NB28_placebo_tests.ipynb # Race-shuffle & pseudo-treatment placebos
│
├── outputs/
│ ├── figures/ # 20+ publication-quality figures (300 DPI PNG)
│ ├── tables/ # 18+ CSV result tables
│ └── paper_figures/ # Alternative figure set from NB26_paper_figures.ipynb
│
├── scripts/
│ ├── build_manuscript.py # Rebuild DOCX manuscript from data
│ ├── generate_publication_figures.py # Generate all 11 paper figures
│ ├── generate_all_missing_outputs.py # RDD diagnostics, balance table, aliases
│ ├── generate_balance_table.py # Covariate balance CSV
│ ├── run_ols_for_nb27.py # OLS regressions for sensitivity bounds
│ ├── run_nb27_real.py # Sensitivity figures with real values
│ ├── run_nb26_direct.py # NB26 direct execution script
│ ├── run_nb28_direct.py # NB28 direct execution script
│ ├── resave_figures_300dpi.py # Verify 300 DPI compliance
│ └── final_verification.py # 42-item submission checklist
│
├── manuscript/
│ └── CATE_HMDA_Final.docx # Submission-ready manuscript (3.9 MB)
│
├── README.md
├── environment.yml # Conda environment specification
└── .gitignore
Run notebooks in sequence from NB17 to NB28. All notebooks use BASE_DIR = Path('D:/Projects/CATE-HMDA-Heterogeneous-Effects') — update this path to match your local setup.
| Notebook | Key Outputs | Est. Runtime |
|---|---|---|
| NB17 — Feature engineering | features_panel.parquet (42.3M rows, 37 features) |
~45 min |
| NB18 — Overlap diagnostics | PS model AUC = 0.729; 98% common support | ~15 min |
| NB19 — DML baseline | Annual ATE table; pooled ATE = −9.39 pp | ~30 min |
| NB20 — PS analysis | Extended overlap diagnostics | ~10 min |
| NB21 — Causal Forest CATE | CATE distribution; subgroup table | ~60 min |
| NB22 — SHAP attribution | Feature importance; AUS = top predictor | ~30 min |
| NB23 — Disparity maps | Income × AUS interaction maps | ~20 min |
| NB24 — RDD | Discontinuity 1.81 pp + 4 diagnostics | ~20 min |
| NB25 — DiD | Event study; DiD = +0.99 pp | ~20 min |
| NB26 — Robustness | DR-Learner = −9.24 pp ✓ | ~60 min |
| NB27 — Sensitivity | Oster δ = 6.87; RV₀ = 0.00512 | ~10 min |
| NB28 — Placebo tests | 17.9× signal ratio ✓ | ~60 min |
Alternatively, use the direct execution scripts in scripts/ for NB26 and NB28 which patch the base path automatically.
HMDA loan application data is publicly available from the Consumer Financial Protection Bureau:
- Source: https://www.consumerfinance.gov/data-research/hmda/
- Years: 2020, 2021, 2022, 2023, 2024
- Format: CSV (~2–5 GB per year)
See data/README_data.md for full instructions on downloading, filtering, and merging the raw HMDA files into features_panel.parquet.
# Clone
git clone https://github.com/Rajveer-code/CATE-HMDA-Heterogeneous-Effects.git
cd CATE-HMDA-Heterogeneous-Effects
# Create conda environment
conda env create -f environment.yml
conda activate cate-hmdaKey dependencies: Python 3.11 · EconML 0.15+ · LightGBM 4.x · Polars 0.20+ · statsmodels · python-docx
# 1. Generate RDD diagnostics, covariate balance, and figure aliases
python scripts/generate_all_missing_outputs.py
# 2. Compute OLS statistics for sensitivity bounds
python scripts/run_ols_for_nb27.py
# 3. Generate sensitivity figures (Oster δ, Cinelli-Hazlett)
python scripts/run_nb27_real.py
# 4. Generate all 11 publication figures
python scripts/generate_publication_figures.py
# 5. Rebuild the manuscript DOCX with all figures embedded
python scripts/build_manuscript.py
# → manuscript/CATE_HMDA_Final.docx
# 6. Run 42-item submission checklist
python scripts/final_verification.py| Year | N (total) | DML Penalty (pp) | SE | 95% CI |
|---|---|---|---|---|
| 2020 | 537,120 | −10.04 | 0.149 | [−10.33, −9.75] |
| 2021 | 562,286 | −9.04 | 0.133 | [−9.30, −8.78] |
| 2022 | 363,996 | −9.65 | 0.163 | [−9.97, −9.33] |
| 2023 | 262,295 | −9.22 | 0.186 | [−9.58, −8.85] |
| 2024 | 274,303 | −8.86 | 0.183 | [−9.22, −8.51] |
| Pooled | 2,000,000 | −9.39 | 0.071 | [−9.52, −9.25] |
| Subgroup | Mean CATE (pp) | 95% CI | % Penalised |
|---|---|---|---|
| Automated AUS | −6.17 | [−6.18, −6.15] | 87.8% |
| Manual/Exempt AUS | −14.79 | [−14.82, −14.77] | 96.5% |
| LTV ≤ 80% | −10.67 | [−10.69, −10.65] | 92.0% |
| LTV > 80% | −6.47 | [−6.49, −6.45] | 88.5% |
| Purchase loans | −6.07 | [−6.08, −6.05] | 86.5% |
| Refinance loans | −9.70 | [−9.72, −9.68] | 92.1% |
| High DTI (≥43%) | −10.23 | [−10.26, −10.21] | 93.3% |
| Income Q1 (<$60K) | −9.52 | [−9.55, −9.50] | 91.8% |
| Income Q5 (>$180K) | −8.56 | [−8.59, −8.52] | 86.8% |
This paper contributes to and extends the following body of work:
| Paper | Venue | Key Finding |
|---|---|---|
| Bartlett, Morse, Stanton & Wallace (2022) | J. Financial Economics | FinTech lenders charge Black/Hispanic borrowers 7.9 bps more |
| Bhutta, Hizmo & Ringo (2025) | J. Finance | 1–2 pp residual denial gap; most explained by observables |
| Fuster, Goldsmith-Pinkham, Ramadorai & Walther (2022) | J. Finance | ML widens within-group racial pricing disparities |
| Chernozhukov et al. (2018) | Econometrics Journal | Double/debiased machine learning |
| Wager & Athey (2018) | JASA | Causal forests for heterogeneous effects |
| Oster (2019) | J. Business & Economic Statistics | Omitted variable bias bounds |
@article{pall2026whobearstheburden,
title = {Who Bears the Burden? Heterogeneous Racial Approval Differentials
in U.S. Mortgage Lending},
author = {Pall, Rajveer Singh},
year = {2026},
note = {Working paper. Gyan Ganga Institute of Technology and Sciences.
Available: https://github.com/Rajveer-code/CATE-HMDA-Heterogeneous-Effects}
}Rajveer Singh Pall
Gyan Ganga Institute of Technology and Sciences, Jabalpur, India
📧 rajveerpall04@gmail.com
🔗 github.com/Rajveer-code
MIT License — see LICENSE for details.
Working paper · May 2026