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Found 7 Skills
Run IV, DiD, and RDD analyses in R with proper diagnostics
Comprehensive Stata reference for writing correct .do files, data management, econometrics, causal inference, graphics, Mata programming, and 20 community packages (reghdfe, estout, did, rdrobust, etc.). Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code.
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
Structured methodology for constructing and verifying mathematical proofs in statistical research
Analyzes disease patterns and health events through epidemiological lens using surveillance systems, outbreak investigation methods, and disease modeling frameworks. Provides insights on disease spread, risk factors, prevention strategies, and public health interventions. Use when: Disease outbreaks, health policy evaluation, risk assessment, intervention planning. Evaluates: Transmission dynamics, risk factors, causality, population health impact, intervention effectiveness.
Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.
Use when "statistical modeling", "A/B testing", "experiment design", "causal inference", "predictive modeling", or asking about "hypothesis testing", "feature engineering", "data analysis", "pandas", "scikit-learn"