AUDIT-GRADE POLICY ANALYSIS.
AUTOMATED.
About Khipu Research Labs
Policy analysis takes weeks. Each method demands careful specification, data cleaning, and robustness checks. Government agencies, nonprofits, academic institutions—they all face the same constraint.
Khipu Research Labs automates what consultants bill thousands to deliver. The platform runs verified causal inference methods—the same frameworks taught in graduate econometrics—but compressed into workflows that survive audit scrutiny. Difference-in-differences for workforce programs. Synthetic control for opportunity zones. Regression discontinuity for education interventions.
We built KRL because policy evaluation shouldn't require a PhD and two fiscal quarters. Every method in the platform traces to peer-reviewed literature. Every connector pulls from verified federal datasets— No synthetic data. No fabricated benchmarks. Just reproducible analysis that holds up when the IG's office asks questions.
This isn't a generic ML platform retrofitted for policy. It's evidence infrastructure purpose-built for the question every agency director actually asks: "Can I trust this analysis when the auditors show up?" If you're running evaluations in-house, KASS gives you the notebooks. If you're ready to automate the pipeline, the platform runs the methods at scale. Either way, you're working with frameworks designed for one thing: policy analysis that holds up under scrutiny.
A Message From the Founder
At KR-Labs, we believe that data has the power to illuminate the structures shaping our societies, economies, and communities. Our mission is to provide accessible, reliable, and actionable intelligence tools that enables organizations, policymakers, and researchers to make informed decisions and drive meaningful impact. We are committed to transparency, methodological rigor, and collaborative problem-solving—whether through our open-core platforms, enterprise solutions, or research initiatives. We welcome your questions, collaboration, and feedback. Together, we can transform complex data into insight, strategy, and tangible change.
Purpose-Built Policy Evaluation Infrastructure
THE PROBLEM: Policy Analysis Runs on Consulting Timelines.
Multiple weeks to months. That's the standard delivery window for a single difference-in-differences evaluation. By the time results land, the legislative session has ended or the grant cycle has closed. Nonprofits running workforce programs need impact data before the next funding round. State agencies evaluating education interventions need results while programs are still operating, not after budget reallocation. Foundations assessing multi-site initiatives need comparable analysis across dozens of grantees without hiring a PhD for each one. The problem isn't lack of methods. It's lack of automation infrastructure purpose-built for policy evaluation.
THE PLAN: Automate What Survives Audit Scrutiny
Khipu Research Labs runs the same causal inference methods a PhD consultant would use—difference-in-differences for workforce programs, synthetic control for opportunity zones, regression discontinuity for education cutoffs—but compressed into automated workflows that handle specification, diagnostics, and robustness checks without manual iteration.WHY THIS MATTERS: Access to Evidence Shouldn't Require Six-Figure Budgets
Policy analysis currently operates on consultant economics. Agencies with resources hire PhDs. Agencies without resources make decisions without evidence. Programs succeed or fail without credible evaluation. Funding flows to politically visible initiatives instead of empirically validated ones.
Khipu doesn't solve bad policy. It solves the information asymmetry that prevents good policy from being identified and scaled.
Workforce programs that reduce unemployment. Education interventions that close achievement gaps. Housing policies that expand affordability. These exist. The bottleneck is proving they work faster than budget cycles and political timelines move.
Insight. Strategy. Impact.
Start with our KASS notebooks. Validate the methods locally. Move to the platform when you need speed without sacrificing audit credibility. We're beta, and stress-testing infrastructure with research partners. No fabricated testimonials. Just documented causal inference methods and radical transparency about the results.
WHY KHIPU RESEARCH LABS
No black boxes. Every automated workflow exists first as an executable KASS notebook. Validate the methods in your environment before committing to platform infrastructure. Policy evaluation you can audit line-by-line.
KASS: Open-Source Policy Methods
Khipu Research Platform: Scale What KASS Validated
Custom Implementation: Enterprise Policy Intelligence
Open-Source Policy Methods
Every causal inference method in the Khipu platform started as a Jupyter notebook—published, documented, reproducible
KASS (Khipu Analytics for Social Science) ships 25+ notebooks covering the methods policy analysts actually use. Run them locally. Fork the repos. Modify the specifications. If you're comfortable in Python and already running evaluations in-house, KASS gives you the frameworks without the platform commitment.
- Verified causal inference notebooks (peer-reviewed methods)
- Federal data connector examples (Census, HUD, BEA, BLS)
- Reproducible workflows you can audit line-by-line
- Community forum for method questions
- Cost: Free. Always.
Khipu Research Platform: Automated Policy Analysis
You need results before the next budget cycle. The platform runs the same methods—peer-reviewed, audit-grade causal inference—but automated into workflows that handle data ingestion, specification testing, robustness checks, and output generation.
This isn't generic ML retrofitted for policy work. Every model in the platform maps to a specific evaluation scenario. Workforce program impact? Difference-in-differences. Geographic policy changes? Synthetic control. Education intervention cutoffs? Regression discontinuity. The methods are identical to what you'd get from a PhD consultant. The timeline compresses from quarters to days.
Who this serves: Nonprofits running multi-site programs. State agencies evaluating workforce initiatives. Foundations assessing grantee outcomes. Any organization that needs credible impact analysis without the consulting invoice.
- 20–65+ causal inference models (tier-dependent)
- Premium federal data connectors (HUD, FHFA, BEA, 50+ verified sources)
- API access for programmatic analysis
- Audit-trail documentation for every specification
- Community or email support (tier-dependent)