Skill UI

A comprehensive guide to establishing credible causal identification in empirical law and economics (L&E). It addresses the high institutional bar required by top journals, detailing how to defend the source of variation (e.g., reforms, governance choices, court assignments) against common threats like endogenous timing, selection bias, and omitted variables. Covers advanced methods including staggered Difference-in-Differences (DiD), Instrumental Variables (IV), and Regression Discontinuity Design (RDD).

A comprehensive guide to defending causal claims in policy and program evaluation, adhering to rigorous econometric standards (e.g., JPAM). It covers advanced methods such as RCT, Difference-in-Differences (DiD), Regression Discontinuity (RDD), Instrumental Variables (IV), and Synthetic Control. This guide emphasizes stating the estimand, defending identification assumptions, and robustly ruling out alternative causal explanations, rather than just writing estimation code.

A comprehensive guide to advanced econometric identification strategies required for high-stakes public finance research. Covers techniques like Regression Discontinuity Design (RDD), Difference-in-Differences (DiD), Bunching, and Instrumental Variables (IV). It stresses the need to defend policy-induced discontinuities and manage threats of endogeneity, improving the credibility of empirical claims.

This skill is designed for creating highly professional and cinematic PowerPoint presentations featuring the 'Morph' transition. It enables smooth, continuous motion for shapes, graphs, and elements as the slide advances, mimicking Keynote-style fluidity. Ideal for complex pitch decks or animated sequences requiring precise cross-slide continuity and advanced animation control.

A comprehensive guide for building robust causal identification strategies in empirical research, particularly for analyzing policy shocks. It provides best practices and checklist items for advanced econometrics methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Double Machine Learning (DML), helping researchers avoid common methodological pitfalls in top-tier academic publications.

This comprehensive framework guides the rigorous design and defense of research in public administration. It covers essential causal inference methodologies, including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RD), and various experimental designs on bureaucrats and citizens. Use this guide when defining identification, responding to reviewer critiques regarding causality, or pre-registering a study, ensuring the design is both academically sound and practically relevant to public policy.

This guide provides detailed best practices for presenting empirical results, specifically tailored for top-tier economics journals like the QJE. It emphasizes shifting from 'table-heavy' to 'figure-forward' visualization, covering techniques for event-study plots, RDD discontinuity plots, and ensuring all exhibits are self-contained, readable, and follow strict academic formatting standards.

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.

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.

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 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 loads and parses a structured handoff document, enabling the user to resume work from a previous session. It systematically extracts the project's scope, files involved, completed tasks, key discoveries, and next steps. Crucially, it summarizes its understanding and mandates user confirmation before taking any action, ensuring accurate context and continuity.