Quantiology Lab turns complex data into credible evidence and clear insight—whether you are evaluating a business strategy, answering a research question, or assessing a policy or program.
Sales, customers, marketing, product, operations
Surveys, administrative data, longitudinal and observational studies
Impact evaluation, trends, populations, outcomes, implementation
Statistics, causal inference, forecasting, reproducible workflows
Business
Performance, customers, marketing, pricing, product, operations, forecasting, experimentation, and decision support.
Research
Research design, survey and administrative data, statistical modeling, reproducible analysis, tables, figures, and results interpretation.
Policy
Program evaluation, policy impact, population trends, subgroup differences, outcome measurement, and evidence synthesis.
Reshape, merge, validate, document, and prepare datasets.
Distributions, trends, groups, relationships, KPIs, tables, and visualizations.
Regression, generalized linear models, longitudinal/panel models, multilevel models, diagnostics, and uncertainty.
Experiments, difference-in-differences, matching, weighting, regression discontinuity, event studies, and program evaluation.
Forecast outcomes, identify risk, classify cases, and build interpretable predictive models.
Publication-quality figures, executive summaries, research tables, technical documentation, and interpretation.
Quantiology Lab brings together experienced statisticians and data scientists. Individual team members bring 5 to 22 years of experience in statistical analysis, data science, and applied quantitative research.
5–22 Years
Experience across statistical modeling, causal inference, forecasting, quantitative research, and complex data workflows.
Multi-domain
Experience with commercial data, research datasets, surveys, administrative records, policy data, program outcomes, and other structured data.
Methods + Interpretation
Selecting appropriate methods, checking assumptions, interpreting results carefully, and communicating findings clearly.
Descriptive statistics, hypothesis testing, regression, GLMs, survival/event-history analysis, panel and longitudinal methods, model diagnostics.
Experiments, matching, inverse-probability weighting, difference-in-differences, regression discontinuity, event studies, impact evaluation.
Weighting, subgroup analysis, linked data, longitudinal data, large datasets, reproducible pipelines, publication-ready outputs.
These examples use synthetic or public-data scenarios. Client data and engagements remain strictly confidential.
Business analytics
Which products and customer groups are driving growth, and where is performance weakening?
Possible deliverables
Academic research
How does an outcome differ across groups and over time after accounting for relevant covariates?
Possible deliverables
Policy evaluation
Did an intervention change outcomes—and for whom?
Possible deliverables
Step 1
Describe objectives, data, deliverables, and deadline.
Step 2
Agree on method, deliverables, timing, and price.
Step 3
Clean, analyze, check, and interpret the data.
Step 4
Receive code, tables, figures, documentation, and written findings.
Business, academic, policy, survey, administrative, or other structured data—we can start by identifying the right analytical approach.
help@quantiologylab.com