Skill UI

This strategy guides researchers in anticipating the specific, rigorous methodological objections raised by top-tier academic referees, especially in applied econometrics. It provides a systematic map connecting econometric designs (like DID, IV, RD) to common threats (e.g., measurement error, bias, specification issues). Users can pre-empt these criticisms by structuring their manuscript to include necessary defenses, robustness checks, and scope limitations before formal submission.

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 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 guide provides a comprehensive framework for structuring and validating the theoretical derivation of statistical methods for academic publishing, particularly in social sciences. It ensures that every claim—including assumptions, consistency, efficiency, and bias—is explicitly stated, traceable to supporting conditions, and linked to empirical or simulation checks, thus maintaining methodological rigor.

A comprehensive guide for designing rigorous Monte Carlo simulation studies, particularly for methods-heavy academic submissions (e.g., SMR). This guide covers specifying the Data Generating Process (DGP) space, selecting non-negotiable competitor methods, choosing appropriate performance metrics (e.g., power, bias, coverage), and critically reporting the limitations and failure regimes of the proposed method to ensure maximum credibility and reproducibility.

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.

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.

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.

This protocol simulates a diverse, anonymous jury of 3 to 5 specialized subagents to reach a committed, non-ambiguous verdict. It is designed to combat cognitive biases like anchoring and groupthink by running blind, independent opinions before structured deliberation. Ideal for high-stakes decisions across product development, architecture, strategy, and vendor choice where a single perspective is insufficient.

This technique forces rigorous critical evaluation by simulating the construction of the strongest, most faithful opposing argument (the 'steel man'). Instead of accepting weak counterarguments or falling into confirmation bias, the process generates a robust adversarial case. It is essential for high-stakes decision reviews, architectural debates, and major proposal vetting, ensuring that the final decision is based on a comprehensive understanding of all risks and conditions for change.