数据与 AI技能

Search Kaggle datasets, competitions, notebooks, and models through its official command-line tool. Download public data into a chosen directory and preserve audit records and file hashes, identifiers used to check downloaded files.

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• Finding Kaggle datasets and inspecting metadata
• Downloading public data into a chosen directory
• Discovering competitions, notebooks, and models

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

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• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

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

Write, debug, and understand Stata code for data cleaning, statistical analysis, causal inference, and charts. The reference covers command syntax, common pitfalls, Mata programming, and community packages for estimation and reporting.

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• Write and debug Stata .do files
• Clean and merge analysis datasets
• Implement regression and causal inference methods

Turn regression results and summary statistics from Stata, R, or Python into LaTeX tables for academic papers. Format model comparisons, decimal places, statistical significance markers, and notes, with guidance for wide tables and inclusion in a paper.

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• Comparing several regression models in one table
• Formatting summary statistics for a paper
• Combining results into a table with multiple panels

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

Choose and interpret models for panel data, which tracks the same firms, people, or countries over time. Use Python, R, or Stata examples to compare fixed and random effects, check assumptions, and select standard errors.

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• Choosing between fixed and random effects
• Estimating entity and time effects together
• Selecting standard errors for correlated observations

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

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 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

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

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

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

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

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

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

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

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

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• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

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

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

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

Plan how to introduce AI into economic research, teaching, or policy analysis. Identify suitable tasks, choose tools with privacy and licensing in mind, and design reproducible data workflows with output checks and ongoing model monitoring.

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• Plan AI adoption for an economics research team
• Design data workflows that protect personal information
• Choose AI tools for teaching or policy analysis

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

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