Skill UI

A comprehensive guide for authors applying machine learning techniques to healthcare. It helps authors frame their research to meet the high standards of top ML4H conferences, covering biomedical data leakage, confounding effects, required evidence standards, and optimal submission strategies.

A comprehensive guide for executing and reporting advanced analysis for high-level academic manuscripts, particularly in management science. It mandates rigorous verification across two lanes: the analytical lane (proving theorems and comparative statics) and the empirical lane (identifying and estimating causal effects using methods like DiD, IV, and RDD). It emphasizes adherence to Data-and-Code-Disclosure policies, ensuring all results are reproducible, verifiable, and meet high methodological standards.

This guide provides a structured methodology for authors to articulate the core contribution of their Information Systems (IS) research for top-tier academic journals, such as MIS Quarterly. It ensures that the paper explicitly states the new knowledge produced (e.g., behavioral mechanisms, design principles, economic effects), justifies the manuscript's length, and rigorously connects theoretical advances with practical and societal implications, thereby avoiding claims of being merely 'incremental' or 'technical' exercises.

This guide provides a rigorous framework for conducting mechanism (mediation/moderation) and heterogeneity analyses for empirical research papers, particularly adhering to high academic standards. It helps researchers move beyond simple correlation to establish causal pathways ('why') and identify differential effects ('for whom') by incorporating theoretically grounded intermediate variables and performing sophisticated econometric tests like causal mediation and interaction effects.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

This skill guides researchers in transforming raw meta-analysis results (e.g., pooled effects, moderator analyses) into a genuine, high-impact theoretical contribution suitable for integrative review journals. It teaches how to interpret conflicting findings, build or refine theoretical models, clearly state scope conditions (boundary conditions), and set concrete future research agendas, ensuring the findings advance the field beyond mere statistics.

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.

Provides detailed guidelines for authors submitting manuscripts to academic journals, specifically addressing the strict limit of 10 tables and figure panels, along with a 10-page supplementary cap. This guide teaches authors how to critically select, consolidate, and present key research results—such as marginal effects, event studies, or decomposition plots—to ensure every exhibit is self-contained, highly clear, and robust enough to pass expert review.

A comprehensive guide for addressing endogeneity, selection bias, and reverse causality in empirical research, specifically tailored for top-tier journals. It details advanced econometric methods (DID, IV, PSM, Heckman) and outlines best practices for conducting rigorous robustness checks, ensuring that identified effects are genuinely causal, not merely correlated.

This comprehensive guide provides methodological frameworks for designing rigorous empirical studies in strategic management. It covers defining the sample, selecting the unit of analysis, validating key measures (performance and strategic choices), and critically, implementing robust identification designs (e.g., DID, IV, Fixed Effects) to address endogeneity and establish causal claims.

A comprehensive guide for conducting and reporting rigorous empirical analysis in academic research, particularly in finance and accounting. It covers selecting appropriate estimators (e.g., DiD, 2SLS, RDD), handling fixed effects, ensuring statistical robustness through placebo and alternative testing, and fulfilling strict data-authenticity and code-access requirements required by top-tier journals.

A rigorous checklist and guide for conducting causal inference in development economics, specifically tailored to the high standards of top-tier journals like The World Bank Economic Review (WBER). It covers advanced econometric designs (RCT, DiD, RD, IV), focusing not just on estimation but on defining the estimand, addressing external validity, general equilibrium effects, and translating local findings into actionable policy recommendations.