데이터·AI 스킬

Guide economic time series analysis from initial plots to forecasting. Use Python, R, and Stata examples to check whether statistical properties remain stable over time, choose models, diagnose errors, and examine relationships between multiple series.

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• Checking trends and stationarity in economic data
• Building ARIMA forecasts and evaluating accuracy
• Examining relationships between multiple time series

Develop Bayesian models for quantitative social science, from choosing priors to reporting posterior results. Guide MCMC, a sampling method for estimating distributions, check convergence, and assess sensitivity for hierarchical models and small datasets.

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• Choosing priors for models with small datasets
• Implementing hierarchical models in Stan or PyMC
• Investigating MCMC convergence problems

Plan, run, and interpret linear regression with guidance on model choices, diagnostic checks, and reporting. Python, R, and Stata examples help examine coefficients, assess assumptions, and choose standard errors that account for different error patterns.

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• Specify a regression model for an empirical study
• Check residuals and correlations among explanatory variables
• Choose robust or clustered standard errors

Choose and implement machine learning methods for estimating causal effects with many control variables or effects that differ across groups. Guidance covers DML, causal forests, and related methods, including diagnostics and limits on interpretation.

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• Estimating average effects with many control variables
• Studying how treatment effects differ across groups
• Implementing cross-fitting for causal estimation

Apply machine learning methods to estimate intervention effects and explore how effects vary across groups. Provides implementation examples, diagnostic checks, and reporting guidance for Double Machine Learning, causal forests, and variable selection.

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• Estimate average effects with many control variables
• Explore differences in effects across groups
• Select control variables using LASSO

Choose and implement methods for estimating causal effects from observational data or policy changes. Guidance covers method assumptions, estimation, diagnostic checks, and robustness tests that examine whether findings hold under alternative analysis choices.

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• Choosing a causal method for evaluating a policy change
• Analyzing policies introduced at different times across regions
• Checking instrumental variable strength and underlying assumptions

Estimate effects by comparing observations just above and below an eligibility threshold using regression discontinuity design. Covers plots, comparison range selection, sharp and fuzzy designs, validity checks, and sensitivity analysis for reporting research findings.

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• Estimate a policy effect near an eligibility threshold
• Analyze treatment with imperfect compliance
• Check manipulation and covariate balance at a cutoff

Plan and implement instrumental variable estimation and propensity score matching, methods for estimating causal effects under different assumptions. Includes Python, R, and Stata examples, diagnostic checks, and guidance on interpreting and reporting results.

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• Choose between instrumental variables and matching
• Implement two-stage least squares estimation
• Check weak instruments and estimation diagnostics

Generate code for economics paper figures, including event studies that show effects over time and coefficient comparisons. Adjust fonts, colors, dimensions, and export settings, with consistent file naming for use in manuscripts.

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• Plot treatment effects over time with confidence intervals
• Compare coefficient estimates across models
• Standardize figure formatting for a journal submission

Analyze time until events such as recovery or relapse using Python. Account for censored observations, where the event has not been observed, create survival curves, compare groups, and estimate risk factors with Cox regression.

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• Analyzing time to death or disease progression in clinical trials
• Comparing survival curves across treatment groups
• Estimating risk factors with Cox regression

Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.

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• Evaluating policy effects with comparison groups
• Checking outcome trends before treatment
• Analyzing policies adopted at different times

Generate Python code to retrieve economic indicators and financial prices from sources including FRED, World Bank, BLS, OECD, IMF, and Yahoo Finance. Scripts include data cleaning, indicator documentation, and error handling for reusable datasets and updates.

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• Downloading GDP, unemployment, and inflation data
• Building datasets across countries and sources
• Automating updates for ongoing research

Prepare survey and administrative datasets for analysis with Python, R, or Stata. Generate cleaning code to handle missing values, duplicates, outliers, and derived variables, with documented transformations and integrity checks that support reproducible research.

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• Clean missing values, duplicates, and outliers in survey data
• Merge multiple data sources for analysis
• Prepare panel datasets that track units over time

Clean survey datasets using their codebooks, which explain variables and response codes. Check missing values and outliers, harmonize variables across years, and prepare combined data with a cleaning report, variable definitions, and recorded transformation history.

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• Checking newly downloaded survey data
• Harmonizing variables across survey years
• Preparing panel data for research

Analyze whether an intervention causes an outcome using DoWhy in Python. Build a graph of causal assumptions, determine whether the effect can be estimated, compare estimates, and test how well results withstand changes to assumptions or data.

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• Estimating an advertising campaign’s causal effect on sales
• Analyzing observational data with confounding factors
• Testing how robust a causal estimate is

Analyze changes before and after an intervention in R, estimate how effects develop over time, and plot results. Choose methods suited to different adoption dates and check assumptions behind comparisons between treated and untreated groups.

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• Plotting effects before and after a policy starts
• Checking whether groups followed similar trends before treatment
• Comparing methods when treatment starts at different times

Improve R code used in empirical economics by finding slow operations, reducing memory use, and optimizing data processing and regressions. It covers large panel datasets and repeated calculations, with checks that optimized code preserves the original results.

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• Optimizing imports and summaries for large survey datasets
• Speeding up fixed-effects regressions on panel data
• Parallelizing Bootstrap and Monte Carlo calculations

Create figures for machine learning papers from text or experiment data. Uses Gemini for architecture diagrams and matplotlib/seaborn for numerical charts, choosing suitable layouts and chart types while preparing consistent styling, export files, and reusable generation scripts.

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• Turning a methods section into an architecture diagram
• Charting model comparisons and ablation results
• Plotting training curves and confusion matrices

Interpret statistical models in R and Python using predictions, comparisons, and marginal effects, which describe how outcomes change with predictors. Get guidance on choosing quantities, evaluating scenarios, summarizing groups, measuring uncertainty, and testing hypotheses.

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• Calculate predicted probabilities for chosen conditions
• Estimate how outcomes change with a predictor
• Compare treatment effects across groups

Run empirical and causal analyses in Python with StatsPAI, from stating assumptions to estimating effects and checking how results change under alternative choices. Produce diagnostic figures, regression tables, and replication bundles for economics, public health, or machine learning studies.

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• Estimating policy or intervention effects
• Comparing public health effect estimates
• Examining how effects differ across groups

Write and debug Python regressions with PyFixest using a reference for model formulas, estimation functions, and standard errors. It also covers extracting results, comparing models, and producing regression tables and plots, including models that account for group differences.

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• Write fixed-effects regression code
• Check clustered standard error settings
• Compare models in tables and plots

Estimate how an intervention affects outcomes using Bayesian causal analysis. The workflow confirms causal assumptions with the user, selects a suitable method, checks whether findings withstand challenges, and reports effects with uncertainty and limitations.

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• Estimating the effect of a policy or intervention
• Mapping causal relationships before analysis
• Selecting a method for observational data

Build and evaluate Bayesian models with PyMC and ArviZ, from choosing initial assumptions to checking predictions. The workflow covers sampling diagnostics, model comparison, and reports that explain results with uncertainty intervals.

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• Choose and check prior distributions
• Investigate sampling and convergence problems
• Compare hierarchical or regression models

Coordinate AI research from literature review to paper drafting. The skill alternates focused experiments with broader reviews of findings, tracks hypotheses and decisions, and uses specialist skills to carry out research tasks and prepare progress reports.

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• Starting a research project from a question
• Testing and comparing multiple hypotheses
• Reassessing direction when experiments stall