Freddie Mac·Data Scientist·Technical Phone Screen·Senior

Senior

Apr 2024Remote

Summary

Interviewed for a Data Scientist role at Freddie Mac and got hit with a deep causal inference question about a real FHFA policy change. One question, but it had five sub-parts and basically covered everything from DiD estimators to how you'd explain prediction intervals to an exec. Dense stuff.

Questions Asked (1)

The FHFA tightened underwriting standards for multifamily loans on April 15, 2024. Design a quasi-experiment to estimate the causal impact on monthly originations and 12-month delinquency rates. Walk through your treatment and control definitions, unit of analysis, DiD approach versus synthetic control, pre-trend testing, falsification checks, spillover concerns, outcome definitions, standard error clustering, how you'd communicate uncertainty to executives, data needs, and how you'd handle policy anticipation and partial compliance.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

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Suggested Approach

Frame the FHFA policy as a natural experiment and propose a difference-in-differences design comparing affected multifamily loans to a credible control group, supplemented by synthetic control for robustness. Walk through each requested element systematically, emphasizing identification assumptions, diagnostics, and practical communication of uncertainty to executives.

Pro tip: Acknowledge that the policy may have heterogeneous effects across lender types and geographies, and propose pre-registering the analysis plan to avoid p-hacking concerns. Also, suggest using a stacked DiD or synthetic control to handle staggered adoption if other policies overlap.

1. Define treatment, control, and unit of analysis

Specify treated units as multifamily loans subject to FHFA underwriting standards (e.g., those with certain LTV/DSCR thresholds) and control as similar loans not subject to the policy (e.g., small community banks or portfolio lenders). Use loan-month or lender-month as the unit of analysis.

2. Choose identification strategy: DiD vs. synthetic control

Propose a difference-in-differences design with fixed effects for loan and time, and a synthetic control method as a robustness check, especially if parallel trends are questionable. Discuss when each is more appropriate.

3. Test pre-trends and conduct falsification checks

Use event-study plots to test for parallel pre-trends and placebo tests (e.g., fake treatment dates, unaffected outcomes) to validate the design. Check for anticipation effects by examining originations before the policy announcement.

4. Address spillovers, compliance, and outcome definitions

Discuss potential spillovers to control group (e.g., lenders shifting to other products) and partial compliance (e.g., some lenders not adhering). Define outcomes clearly: monthly originations (count or volume) and 12-month delinquency rates (e.g., 60+ days past due).

5. Specify inference and communicate uncertainty

Cluster standard errors at the lender or state level to account for correlation. For executives, present confidence intervals and economic significance, and use simulations or Bayesian methods to convey uncertainty in business terms.

Key Points to Mention

Treatment definition based on loan characteristics or lender type affected by FHFA standards
Use of loan-level data with monthly observations and fixed effects
Parallel trends assumption and event-study evidence
Synthetic control as a robustness check when control group is questionable
Clustering standard errors at the lender or geographic level
Handling anticipation by excluding pre-announcement periods or using leads

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.