Skill UI

This skill guides authors in transforming empirical research findings into a robust theoretical contribution for Public Administration. It emphasizes articulating clear mechanisms, defining necessary scope conditions, and providing actionable "Evidence for Practice," ensuring the work advances both academic theory and real-world policy.

This comprehensive guide details the rigorous standards required to defend the identification of parameters, estimands, or causal effects in quantitative economic research. It provides specific, advanced methodologies and checklists for three major branches: structural modeling, empirical causal design (e.g., DID, IV, RDD), and experimental studies. It ensures the work meets the high quantitative bar of top-tier journals by demanding explicit data-to-object mapping and robust diagnostic testing.

A comprehensive guide for authors to pinpoint the unique quantitative contribution (the 'delta') of their manuscript. It teaches how to articulate the specific gap closed relative to prior empirical, structural, and methodological work, ensuring the paper is correctly positioned for target journals like QE, distinguishing it from related fields.

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

A comprehensive methodological guide for structuring the robustness checks and online appendix of high-level empirical economics manuscripts, specifically tailored to the standards of the Quarterly Journal of Economics (QJE). It provides a structured approach to defending the identification assumption, selection, measurement, and inference, ensuring the paper anticipates and addresses potential referee critiques.

This guide provides detailed best practices for presenting empirical results, specifically tailored for top-tier economics journals like the QJE. It emphasizes shifting from 'table-heavy' to 'figure-forward' visualization, covering techniques for event-study plots, RDD discontinuity plots, and ensuring all exhibits are self-contained, readable, and follow strict academic formatting standards.

A comprehensive guide on transforming clean, empirical coefficients into theoretically robust and broadly impactful economic arguments. It teaches how to construct the necessary conceptual framework, articulating the underlying mechanism and connecting estimates to welfare or policy outcomes, suitable for high-impact academic publications.

This skill polishes scholarly prose, abstracts, and introductions to align with the narrative-driven, ambitious style of the Quarterly Journal of Economics (QJE). It focuses on making the core research contribution immediately legible to a general-interest, non-specialist reader, ensuring the big idea lands fast, even in long-form empirical papers. The skill guides users on structuring the 'introduction arc,' stating findings with concrete numbers, and minimizing technical jargon.

This tool guides authors writing on industrial organization, market structure, regulation, and antitrust. It provides a rigorous framework for evaluating whether a manuscript meets the high standards of top-tier IO journals by assessing theoretical models, empirical identification, and policy significance. Use it for venue selection and manuscript re-framing.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

This comprehensive guide helps authors structure the inferential backbone of economic papers for top journals. It provides detailed checklists for theoretical models (assumptions, regularity), empirical studies (causal design, identification), and computational methods (convergence, asymptotics), ensuring claims are robust and credible across different paper types.

This guide establishes the rigorous policy for submitting computational and empirical papers to academic journals. It mandates that authors provide sufficient data, source code, and detailed documentation (including execution order, random seeds, and OS used) to ensure that research findings can be fully reproduced by others, adhering to global standards like Option C.