Skill UI

This skill provides bounded, local exploratory data analysis for diverse scientific data files, including CSV, JSON, HDF5, and image metadata. It rigorously audits data structure, detects missing values, and identifies outliers in a secure, sandboxed environment. It is designed exclusively for pre-modeling data integrity checks and must not be used to infer scientific meaning or make causal claims.

A comprehensive framework for transforming raw observations into rigorous, evidence-bounded, and testable research plans. It guides users through establishing research questions (PICO/PECO), formulating candidate hypotheses and rival explanations, defining causal estimands, and creating preregistration-ready analysis plans, all while maintaining strict ethical and scientific guardrails.

This skill guides users through the critical steps of experimental design, helping plan studies before data collection. It covers selecting appropriate methodologies—such as Factorial, Blocked, Crossover, and DOE designs—and generating randomized layouts. It ensures that experiments are structured correctly to isolate effects, enabling reliable causal claims and robust data interpretation.

This tool performs pathfinder traversal across a knowledge graph starting from a specified seed entity. It expands outward through causal edges, calculates path relevance scores, and prunes low-similarity branches. It is ideal for conducting impact analyses, tracing dependency chains, or understanding complex relationships within a structured knowledge base.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.

A comprehensive guide for formatting and presenting empirical results (tables and figures) for submission to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures that main causal estimates are immediately legible to referees by adhering to rigorous academic standards, including presenting standard errors, confidence intervals, and specific exhibit structures like event-study plots and balance tables.

This guide outlines the rigorous methodological standards for empirical identification in macroeconomics, crucial for submitting to top-tier journals. It covers advanced techniques such as Structural VAR (SVAR), Local Projections (LP), and handling high-frequency/micro-data shocks. It ensures authors explicitly defend the mapping from raw data to dynamic causal objects, addressing critical issues like endogeneity, anticipation, and structural breaks, and providing execution checklists for modern econometric tools (DiD, IV, RDD).

This guide provides stringent methodological standards for establishing credible causal claims in empirical policy manuscripts. It covers advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Randomized Controlled Trials (RCT)—required to move beyond mere correlation and prove that the observed effect is genuinely caused by the policy intervention.

A comprehensive strategy guide for authors submitting policy-evaluation papers to top economics journals. It details how expert referees evaluate manuscripts, focusing on four critical areas: policy interest, causal credibility, welfare reading, and robustness. This tool helps authors proactively anticipate and strengthen potential weaknesses before submission or during revision, significantly increasing the paper's chances of acceptance.