Skill UI

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 comprehensive methodological guide for designing rigorous academic studies in accounting. It details advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and experimental setups—to establish credible causal identification. Use this guide when your research challenge is solving endogeneity or isolating a specific economic channel from complex, non-random variables.

This skill details the rigorous process of converting raw statistical outputs into self-contained, publication-ready tables and figures for top-tier academic journals. It ensures compliance with strict house styles, covering sample selection, descriptive statistics, main regression results, and specific formatting for DiD, RDD, and event studies, all while adhering to strict page limits and providing economic magnitude interpretation.

This guide assists researchers in determining the suitability of their study for The Lancet Public Health. It outlines the scope, emphasizing population-level interventions, global epidemiology, and health policy. It details stringent requirements for methodology, reporting guidelines (STROBE, CONSORT, PRISMA), addressing biases, and adhering to specialized journal house styles and submission protocols.

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.

This guide outlines the rigorous standards for establishing causal claims in development economics and social sciences. It covers robust quantitative methods (like DID, IV, RDD, RCT) and structured qualitative/mixed-methods approaches. The core principle is that the logical route from evidence to claim must be explicitly defensible and matched to the strength of the assertion, avoiding over-claiming.

A comprehensive guide for preparing replication and transparency packages for World Development manuscripts. It provides checklists and best practices for both quantitative (code reproducibility, data provenance, de-identification) and qualitative (evidence-to-claim trail, methods appendix) research. It addresses ethical constraints and the unique challenges of double-anonymized review.

This comprehensive guide provides authors with structured frameworks for assessing robustness and trustworthiness in World Development manuscripts. It guides users to organize checks by identifying specific threats (e.g., specification dependence, measurement error) in quantitative work, or demonstrating methodological rigor (e.g., triangulation, negative-case analysis) in qualitative work. It ensures findings are thoroughly vetted against alternative explanations.

This guide helps scholars rigorously defend the methodological design of academic papers, particularly in International Relations and Comparative Politics. It covers advanced techniques such as process tracing, case selection logic, quantitative cross-national panel data analysis (DID, IV, RDD), and experimental design. Its core purpose is to strengthen causal claims by explicitly addressing selection bias, defining scope conditions, and ruling out major rival explanations.

This skill guides the creation of self-contained, highly accessible, and reproducible exhibits (tables, figures, maps) for World Politics manuscripts. It addresses strict length limits by differentiating between core arguments (in the article) and supplementary material (online appendix). It emphasizes visual communication excellence, moving beyond simple coefficient tables to forest plots and event studies to convey magnitude and uncertainty effectively.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.