Publications / CATE — Mortgage Lending

Under journal review2026

Who Bears the Burden? Heterogeneous Racial Approval Differentials in U.S. Mortgage Lending: Causal Forest DML on 42 Million HMDA Applications

Rajveer Singh Pall

Not whether an average penalty exists, but who bears it — and through which underwriting channel.

SSRN preprint ↗Code ↗

The discovery in one figure

A HEADLINE REPORTS ONE AVERAGE —−9.39pp, "the gap"but it shatters0 · no penalty90.7% penalisedmanual −14.79automated −6.17The underwriting channel — not the applicant — explains who lands where in this distribution (SD 8.47 pp).
The average gap is a lie — the underwriting channel decides who bears the burden.

The paper in five minutes

Knowing the average approval gap is not enough — averages hide who actually pays. Using methods from modern causal inference (the same family behind clinical-trial analysis), this paper estimates the approval penalty for each applicant profile across 42 million mortgage applications. The distribution is wide: nine in ten Black applicants face some estimated penalty, and the decisive factor is not the applicant but the process — applications handled by human underwriters carry more than double the penalty of those decided by automated systems. That points the fairness question at something a regulator can act on: how applications are routed.

The research question

Average disparity estimates hide distribution: which applicant profiles carry the largest conditional racial approval penalty, and is the mechanism applicant- or lender-controlled?

How it works

Partially linear Double Machine Learning with LightGBM nuisances and 5-fold cross-fitting; causal-forest CATE estimation with SHAP attribution; placebo, Oster, and Cinelli–Hazlett sensitivity analyses; within lender-year comparisons.

  1. 01
    Engineer at scale42M HMDA applications → 2M/1.5M estimation samples
  2. 02
    Isolate the differentialdouble machine learning, LightGBM nuisances, 5-fold cross-fitting
  3. 03
    Map who bears itcausal forest estimates the penalty per applicant profile
  4. 04
    Find the mechanismmanual vs automated underwriting · same lender, same year
  5. 05
    Attack the resultplacebo shuffles · Oster bounds · Cinelli–Hazlett sensitivity

Experimental results

Pooled conditional differential −9.39 pp with wide heterogeneity (CATE SD 8.47 pp); 90.7% of Black applicants face a negative estimated effect. The channel is decisive: manual underwriting −14.79 pp vs automated −6.17 pp; −7.13 pp persists within the same lender and year.

Conditional approval penalty by underwriting channel
Manual underwriting
−14.79 pp
Pooled (all channels)
−9.39 pp
Same lender, same year
−7.13 pp
Automated systems
−6.17 pp

Bar length = size of the estimated Black–White differential (all negative). The channel, not the applicant, is decisive.

  • −9.39 ppconditional differential (pooled DML)
  • −14.79 vs −6.17manual vs automated underwriting (pp)
  • 90.7%of Black applicants with negative effect

Stated honestlyLevel treated as an upper bound (no credit scores in HMDA); the channel contrast is the robust object.

Figures from the paper

Double-machine-learning estimates of the conditional approval differential across specifications.
Fig. 1Double-machine-learning estimates of the conditional approval differential across specifications.
The distribution of individual-level estimated effects: wide heterogeneity, overwhelmingly negative.
Fig. 2The distribution of individual-level estimated effects: wide heterogeneity, overwhelmingly negative.
SHAP attribution over the causal-forest estimates: what drives who bears the penalty.
Fig. 3SHAP attribution over the causal-forest estimates: what drives who bears the penalty.

Figures generated by the paper's own pipeline — reproducible from the repository.

What this changes

Points the fairness question at an actionable mechanism — lender-controlled routing and handling — rather than at applicant characteristics.

Resources

SSRN preprint ↗Code ↗
  • Causal Inference
  • Fairness
  • Trustworthy ML

Citation

Manuscript under review — citation will be posted on acceptance. Reach me at rajveerpall04@gmail.com.

Where this leads next