Skill UI

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 guide assists in building a robust theoretical framework for research policy manuscripts. It moves beyond simple empirical measurement by structuring the argument using the innovation-mechanism chain (antecedent → mechanism → outcome). It teaches how to connect firm-level findings up to the systemic policy or institutional level, ensuring the contribution is theoretically advanced and legible across disciplines (e.g., economic, managerial, STS).

This comprehensive guide details advanced econometric methodologies required for rigorous causal inference studies. It covers how to defend both the causal identification strategy (DiD, RD, IV) and the quality of variables (handling measurement error, index construction). Learn to meet the stringent standards of top-tier journals by applying techniques like staggered DiD estimators, density testing, and attenuation correction.

This guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.

This guide is designed for advanced academic research, helping scholars evaluate whether their current research idea meets the rigorous standards of top-tier economics journals (e.g., REStud). It teaches how to distill complex findings into a single, original, and technically excellent contribution, diagnosing potential weaknesses in novelty, scope, and general interest.

This skill polishes the prose and structure of advanced academic manuscripts for top-tier journals. It focuses on enhancing clarity, conciseness, and logical flow, especially optimizing the introduction and abstract to ensure the core research contribution is immediately evident and highly readable, without altering the original results or economic models.

A comprehensive guide for executing rigorous empirical accounting studies. This resource details best practices for advanced econometric techniques, including correct standard error clustering (firm-level, two-way clustering), executing identification strategies (DiD, RD, IV/2SLS), validating accounting constructs, and performing extensive robustness checks (falsification, placebo tests). It also emphasizes data provenance and reproducibility, critical for top-tier academic publications.

A comprehensive guide for establishing credible causal inference in accounting and finance research. This methodology covers advanced techniques including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and experimental design. It instructs users on how to move beyond simple controls, defining the specific identifying variation necessary for publishing rigorous, causality-focused academic work.

This guide outlines best practices for developing self-contained, rigorous tables and figures for empirical academic research, particularly in accounting. It covers the complete exhibit set—including variable definitions, descriptive statistics, main results, and advanced diagnostic plots (e.g., DiD event-time plots)—ensuring the work withstands intense academic scrutiny.

This guide is designed for systematic reviews and meta-analyses, providing a rigorous framework to stress-test the robustness and completeness of reported findings. It mandates advanced checks such as saturation evidence, risk-of-bias appraisal, and statistical diagnostics (e.g., heterogeneity, publication bias) to ensure conclusions are not artifacts of omission or methodological weakness. Essential for high-level academic research.

A comprehensive methodological guide detailing high standards for causal inference in finance research, specifically tailored for top journals like RFS. It covers advanced techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and asset-pricing identification, ensuring designs meet rigorous standards for exogenous variation and endogeneity control.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.