Visual Research Guides
A complete visual curriculum for causal inference and study design — from survival curves to directed acyclic graphs. Built for researchers, reviewers, and students who want methodology they can actually use.
Foundations
Before you analyze, you need to design. Before you design, you need to read survival curves. Start here.
Kaplan-Meier Survival Analysis
Reading survival curves correctly — single-curve patterns, comparative scenarios, PH violations, competing risks, and the number-at-risk table you're probably ignoring.
Clinical Trial Randomization
Simple, block, stratified, cluster, adaptive, and response-adaptive randomization — with allocation concealment, clinical equipoise, and the decision framework.
The Causal Inference Series
Six methods. Six visual guides. From matching and weighting through natural experiments and structural thinking. Each one covers the method, its assumptions, diagnostics, failure modes, and what reviewers expect in 2026.
Propensity Score Matching
6 matching strategies, the diagnostic trinity (Love Plot, Balance Table, Variance Ratio), estimand clarity, and why p-values are not balance diagnostics.
Inverse Probability Weighting
ATE/ATT/ATC/ATO estimands, the 9-step workflow with STOP conditions, pseudo-populations, weight diagnostics, and the E-value for unmeasured confounding.
Instrumental Variables
The LATE framework, compliance types, the Forbidden Regression, weak instrument diagnostics, sensitivity analysis, and when IV is the wrong tool.
Difference-in-Differences
Parallel trends (not similar levels), TWFE failures in staggered adoption, event study plots, and why Callaway & Sant'Anna matters for modern DiD.
Regression Discontinuity Design
Sharp vs fuzzy RDD, bandwidth selection (too narrow / too wide / MSE-optimal), five mistakes that kill your RDD, and what reviewers expect.
Directed Acyclic Graphs
Forks, chains, colliders, M-Bias, Z-Bias, the backdoor criterion, the obesity paradox, and why if you can't draw the DAG, you don't know what you're adjusting for.
AI & Research Architecture
Where research methodology meets AI systems design.
7-Layer Memory Architecture
A multi-layer memory system for persistent AI agents — from profile K-V stores through episodic logs to long-term curated memory. Designed, tested, and validated in production.