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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.

79 guides 76 causal inference methods 2 foundational methods 1 AI architecture

Anas H. Alzahrani, MD PhD MPH Department of Preventive Medicine and Public Health Faculty of Medicine, King Abdulaziz University

Foundations

Before you analyze, you need to design. Before you design, you need to read survival curves. Start here.

Guide 01

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.

Survival Analysis Competing Risks CIF

Guide 02

Clinical Trial Randomization

Simple, block, stratified, cluster, adaptive, and response-adaptive randomization — with allocation concealment, clinical equipoise, and the decision framework.

RCT Design Allocation Adaptive

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.

Guide 03 — Causal Inference

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.

PSM ATT vs ATE Balance

Guide 04 — Causal Inference

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.

IPW Estimands Pseudo-population

Guide 05 — Causal Inference

Instrumental Variables

The LATE framework, compliance types, the Forbidden Regression, weak instrument diagnostics, sensitivity analysis, and when IV is the wrong tool.

IV LATE 2SLS

Guide 06 — Causal Inference

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.

DiD TWFE Event Study

Guide 07 — Causal Inference

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.

RDD Bandwidth Local ATE

Guide 08 — Structural Thinking

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.

DAGs Collider Bias Backdoor

AI & Research Architecture

Where research methodology meets AI systems design.

Guide 09 — AI Architecture

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.

AI Agents Memory Architecture

Advanced Causal Inference

Longitudinal causal problems are where a lot of observational papers quietly break. These guides cover the designs and diagnostics you need when treatment and confounding evolve over time, how to use marginal structural models when standard adjustment starts eating the treatment pathway, how to emulate the trial you wish you had actually run, how to think carefully about mechanisms, how not to let the analytic sample quietly hijack the causal question, what happens when the variables themselves are fuzzier than the prose admits, why events that happen first can completely rewrite the endpoint story, and how to pressure-test causal claims with negative controls before you start believing your own model.

Guide 10 — Causal Inference

Target Trial Emulation Failure Modes

A visual guide to the design mistakes that make observational treatment comparisons look cleaner than they are. Covers time zero, immortal time bias, grace periods, clone-censor-weight, and the reviewer checks that separate a real emulation from retrospective cosplay.

Target Trial Emulation Time Zero Clone-Censor-Weight

Guide 11 — Causal Inference

Time-Varying Confounding

Why standard adjustment can fail when later covariates both predict future treatment and are changed by prior treatment. A practical visual guide to treatment-confounder feedback, marginal structural models, stabilized weights, and the reviewer checks that actually matter.

MSM IPTW Longitudinal Causality

Guide 12 — Causal Inference

Mediation Analysis

A visual guide to separating total, direct, and indirect effects without overclaiming mechanism. Covers pathway-specific thinking, the assumptions that usually fail, and why mediator-outcome confounding can quietly break the whole analysis.

Direct vs Indirect Effects Mechanisms Post-treatment Bias

Guide 13 — Causal Inference

G-computation

A practical visual guide to counterfactual prediction and standardization: fit the outcome model, simulate intervention worlds, average over the target population, and check where model dependence can quietly bite.

Standardization Counterfactual Prediction Outcome Modeling

Guide 14 — Causal Inference

Sensitivity Analysis for Unmeasured Confounding

How to ask whether hidden bias could actually overturn your estimate. Covers E-values, quantitative bias analysis, Rosenbaum bounds, benchmarking, and the difference between real robustness and robustness theater.

Unmeasured Confounding E-value Robustness

Guide 15 — Causal Inference

Selection Bias in Causal Inference

A visual guide to the moment your analytic sample stops representing the causal contrast you think you are estimating. Covers enrollment bias, informative censoring, complete-case selection, collider-driven restriction, and the checks that keep the counting process from becoming the whole story.

Selection Bias Informative Censoring Collider Bias

Guide 16 — Causal Inference

Measurement Error in Causal Inference

A visual guide to what happens when the variable in your model is only a blurry stand-in for the variable in the world. Covers exposure misclassification, outcome ascertainment, residual confounding from weak proxies, and why “probably toward the null” is not a serious measurement strategy.

Measurement Error Misclassification Residual Confounding

Guide 17 — Causal Inference

Double Machine Learning

A visual guide to using flexible prediction without turning the causal estimand into mush. Covers cross-fitting, orthogonalization, nuisance models, and the limits no algorithm can charm its way around.

Double ML Cross-Fitting Orthogonalization

Guide 18 — Causal Inference

Competing Risks in Clinical Research

A visual guide to the moment death stops being a censoring convenience and becomes part of the estimand. Covers cumulative incidence, cause-specific hazards, Fine-Gray, and how to avoid mistaking fewer observed events for better outcomes.

Competing Risks CIF Fine-Gray

Guide 19 — Causal Inference

Negative Controls in Clinical Observational Research

A visual guide to one of the most useful bias checks in observational research: ask what should stay null, then see whether your design can keep it that way. Covers negative control outcomes, negative control exposures, shared bias structure, and how a bad control should make your causal claims noticeably humbler.

Negative Controls Residual Bias Falsification

Guide 20 — Causal Inference

Marginal Structural Models in Clinical Research

A visual guide to the moment standard adjustment starts adjusting away the treatment story. Covers treatment-confounder feedback, inverse probability weighting, stabilized weights, informative censoring, and the reviewer checks that keep longitudinal causal claims from becoming beautifully formatted fiction.

Marginal Structural Models IPW Longitudinal Causality

Guide 21 — Causal Inference

Principal Stratification in Clinical Research

A visual guide to the questions investigators keep asking in the wrong subgroup. Covers latent principal strata, truncation by death, the survivor average causal effect, and why “among adherers” is usually not the causal shortcut people hope it is.

Principal Stratification SACE Truncation by Death

Guide 22 — Causal Inference

Active Comparator New-User Design in Clinical Research

A visual guide to building a drug comparison before the model starts improvising. Covers plausible alternative comparators, initiation-aligned time zero, true new users, and why treated-versus-untreated cohorts often estimate prescribing logic with a hazard ratio attached.

Active Comparator New-User Design Channeling Bias

Guide 23 — Causal Inference

Front-Door Criterion in Clinical Research

A visual guide to one of causal inference's most elegant and most overclaimed ideas. Covers full mediation, mediator-path confounding, why front-door identification is rare in clinical data, and how to tell a real mechanism design from mediator-shaped wishful thinking.

Front-Door Criterion Mediation Unmeasured Confounding

Guide 24 — Causal Inference

Fuzzy Regression Discontinuity at Clinical Treatment Thresholds

A visual guide to what happens when a guideline cutoff changes the odds of treatment without perfectly assigning it. Covers the first-stage jump, local causal interpretation near the boundary, manipulation risks, and why threshold-based clinical decisions are useful instruments only when the rule actually bites.

Fuzzy RDD Clinical Thresholds Local Effect

Guide 25 — Causal Inference

Case-Time-Control in Clinical Research

A visual guide to repairing case-crossover analyses when exposure prevalence drifts over calendar time. Covers hazard and control windows, matched controls, secular exposure trends, and the reviewer checks that separate a trigger design from calendar-time confounding with nicer formatting.

Case-Time-Control Case-Crossover Exposure Time Trends

Guide 26 — Causal Inference

Non-Collapsibility vs Confounding in Clinical Research

A visual guide to the clinical mistake of treating every crude-to-adjusted odds-ratio shift as proof of confounding control. Covers true confounding, non-collapsibility without confounding, conditional versus marginal interpretation, and the reporting checks that keep logistic-regression folklore out of the discussion section.

Odds Ratios Hazard Ratios Interpretation

Guide 27 — Causal Inference

Positivity Violations in Dynamic Treatment Strategies

A visual guide to the point where longitudinal causal ambition outruns clinical data support. Covers evolving ICU severity states, near-deterministic treatment decisions, extreme inverse-probability weights, and how to narrow the estimand before a dynamic strategy analysis starts negotiating with absence.

Positivity Dynamic Treatment Strategies MSMs

Guide 28 — Causal Inference

Truncation by Death in Clinical Research

A visual guide to the deceptively simple move of comparing outcomes only among survivors. Covers why post-treatment survival reshapes the analysis set, how truncation by death differs from routine missingness, and which alternatives keep later functional endpoints clinically honest.

Survivor Bias QoL Outcomes Estimands

Guide 29 — Causal Inference

Self-Controlled Case Series in Clinical Research

A visual guide to the self-matched design people love for vaccine safety and drug-trigger questions. Covers transient exposures, acute outcomes, event-dependent exposure, protopathic bias, and the timeline assumptions that decide whether SCCS is elegant or just elegantly wrong.

SCCS Self-Matched Design Acute Events

Guide 30 — Causal Inference

G-Estimation of Structural Nested Models in Clinical Research

A visual guide to one of causal inference's more exacting tools for sequential treatment decisions. Covers treatment-confounder feedback, blip models, blipped-down outcomes, and how to tell whether your repeated-treatment question needs structural nested thinking instead of ordinary adjustment with better manners.

G-Estimation Structural Nested Models Time-Varying Confounding

Guide 31 — Causal Inference

Proximal Causal Inference in Clinical Research

A visual guide to using proxy variables when the confounder you care about is only partly visible in the dataset. Covers treatment-inducing versus outcome-inducing proxies, bridge-function logic, timing discipline, and the difference between a rescue method and residual-confounding theater.

Proximal CI Proxy Variables Unmeasured Confounding

Guide 32 — Causal Inference

Landmark Analysis in Clinical Research

A visual guide to the conditional survival comparison that looks rigorous until people forget who had to survive long enough to enter it. Covers prespecified landmarks, responder-versus-non-responder oncology examples, immortal-time cleanup, and the survivor-selection warning label every paper should wear.

Survival Analysis Immortal Time Bias Conditional Estimands

Guide 33 — Causal Inference

Clone-Censor-Weight in Clinical Research

A visual guide to the grace-period treatment strategy that saves target trial emulations from delayed-labeling nonsense. Covers baseline cloning, deviation censoring, IPCW, and the exact reviewer questions to ask before immortal time bias sneaks into the conclusions wearing a respectable tie.

Target Trial Emulation Grace Periods Immortal Time Bias

Guide 34 — Causal Inference

Repeated Eligibility in Target Trial Emulation

A visual guide to the recurring decision-point problem that appears when patients can become newly eligible again and again. Covers sequential trial emulation, visit-specific time zero, re-entry rules, pooled analysis with clustering, and why “ever versus never” is often just a slower way to say “we lost track of the design.”

Target Trial Emulation Repeated Eligibility Time Zero

Guide 35 — Causal Inference

Informative Visit Process Bias in EHR-Based Clinical Causal Inference

A visual guide to the observational problem where sicker patients are seen, measured, and recorded more often than everyone else. Covers visit-driven covariate updating, selective outcome detection, treatment changes tied to encounter intensity, and the diagnostics that keep “more data” from masquerading as “more truth.”

EHR Data Observation Process Ascertainment Bias

Guide 36 — Causal Inference

Consistency and Treatment Versions in Clinical Research

A visual guide to the causal assumption people invoke with one sentence and violate with an exposure label. Covers treatment-version heterogeneity, vague intervention definitions, clinically distinct dosing and timing strategies, and the reporting checks that keep "treated" from meaning six different things at once.

Consistency Estimands Intervention Definition

Guide 37 — Causal Inference

Treatment Switching and Rescue Therapy in Clinical Causal Inference

A visual guide to the post-baseline mess that turns one treatment comparison into three different estimands. Covers crossover, rescue medication, treatment-policy versus per-protocol questions, informative censoring, and when marginal structural models earn their keep.

Treatment Switching Estimands MSMs

Guide 38 — Causal Inference

Overlap Weighting in Clinical Research

A visual guide to the propensity-score weighting strategy that stops asking the tails to do the middle's job. Covers the overlap population, why weights favor clinical equipoise rather than near-deterministic treatment choices, how it differs from ATE-style IPW, and the diagnostics that keep a stable estimate from becoming a vague claim.

Propensity Scores Overlap Weighting Comparative Effectiveness

Guide 39 — Causal Inference

Intercurrent Events Under the Estimand Framework in Clinical Research

A visual guide to the post-baseline events that quietly rewrite a treatment question. Covers treatment-policy, hypothetical, composite, while-on-treatment, and principal-stratum strategies, with clinical framing for rescue therapy, discontinuation, crossover, and death before nonfatal endpoints.

Estimands Intercurrent Events Clinical Trials

Guide 40 — Causal Inference

Prevalent User Bias and Washout Windows in Clinical Research

A visual guide to the design mistake that lets treatment history rewrite baseline before the study even begins. Covers why ongoing users look deceptively sturdy, how washout windows make initiation effects more believable, and the reviewer checks that separate a new-user design from refill-gap folklore.

Pharmacoepidemiology New-User Design Washout Windows

Guide 41 — Causal Inference

Missing Data in Causal Inference for Clinical Research

A visual guide to the moment missing data stops being housekeeping and starts becoming selection. Covers missing confounders, informative dropout, when multiple imputation fits, when censoring weights fit better, and the reviewer checks that keep an estimand from shrinking to the patients easiest to observe.

Missing Data Selection Bias Clinical Research

Guide 42 — Causal Inference

Calendar-Time Confounding in Rapidly Changing Treatment Eras

A visual guide to the treatment comparison that quietly becomes a comparison between different versions of medicine. Covers secular drift, contemporaneous comparators, evolving supportive care, and the reviewer checks that separate therapy effects from historical timing effects.

Secular Trends Comparative Effectiveness Clinical Research

Guide 43 — Causal Inference

Channeling Bias in Newly Launched Therapies

A visual guide to the adoption pattern that becomes a treatment effect if you are not careful. Covers refractory patients, contraindication-driven routing, selective early adopters, and the design checks that separate prescribing behavior from drug performance.

Pharmacoepidemiology Bias Clinical Research

Guide 44 — Causal Inference

Depletion of Susceptibles in Clinical Research

A visual guide to the moment later follow-up looks safer because the patients most vulnerable to harm already left the risk set. Covers early toxicity, selective attrition, survivor-enriched exposure windows, and the reviewer checks that keep persistence from masquerading as protection.

Drug Safety Selection Bias Clinical Research

Guide 45 — Causal Inference

Left Truncation and Delayed Entry in Clinical Research

A visual guide to the moment a cohort becomes observable only after patients already had to survive, stay event-free, or remain in care long enough to enter the dataset. Covers misaligned time zero, survivor-conditioned risk sets, landmark alternatives, and the review questions that keep delayed entry from masquerading as treatment benefit.

Survival Analysis Selection Bias Clinical Research

Guide 46 — Causal Inference

Index Event Bias in Secondary Prevention Clinical Research

A visual guide to the selection problem that starts the moment a cohort is defined by already having had the first event. Covers paradoxical post-event associations, conditional interpretation, secondary prevention cohorts, and the reviewer checks that keep recurrence analyses from drifting into broad etiologic claims.

Secondary Prevention Selection Bias Clinical Research

Guide 47 — Causal Inference

Incremental Propensity Score Interventions in Clinical Research

A visual guide to policy-shift estimands for settings where treat-all contrasts are clinically implausible or poorly supported by overlap. Covers odds-shift interventions, why they help when positivity is strained, realistic uptake questions, and the reviewer checks that keep an advanced method tied to a real clinical decision.

Positivity Policy Shift Clinical Research

Guide 48 — Causal Inference

Generalized Propensity Scores for Continuous Exposures in Clinical Research

A visual guide to dose-response causal questions when exposure is measured on a continuum rather than as a simple treated-versus-untreated choice. Covers conditional exposure density, overlap across dose regions, tail instability, treatment versions, and the reviewer checks that keep a smooth curve from outrunning the data.

Dose-Response Propensity Scores Clinical Research

Guide 49 — Causal Inference

Confounding by Contraindication in Clinical Research

A visual guide to the bias that appears when treatment is withheld from the frailest or riskiest patients for good clinical reasons. Covers anticoagulation and bleeding-risk routing, why untreated groups are often prognostically different before follow-up starts, overlap failure, and the design checks that keep clinical prudence from masquerading as treatment effect.

Comparative Effectiveness Confounding Clinical Research

Guide 50 — Causal Inference

Exposure-Induced Mediator-Outcome Confounding in Clinical Mediation Studies

A visual guide to the mediation failure mode that appears when treatment changes a post-baseline variable that then affects both the mediator and the outcome. Covers anti-inflammatory treatment and disease-activity trajectories, why naive adjustment can misidentify direct and indirect effects, and when interventional effects or g-methods are safer than a standard natural-effects decomposition.

Mediation Analysis Post-Treatment Bias Clinical Research

Guide 51 — Causal Inference

Stochastic Interventions in Clinical Research

A visual guide to probability-shifting causal policies for studies where real implementation changes treatment uptake rather than forcing universal treatment. Covers the difference between static, deterministic dynamic, and stochastic interventions, why stewardship and guideline-uptake questions often fit this estimand better, and how to keep a probability-shift effect from being misread as a treat-all contrast.

Estimands Policy Interventions Clinical Research

Guide 52 — Causal Inference

Death Before the Outcome: Intercurrent Event vs Competing Risk vs Truncation by Death

A visual comparison guide to the three analytic meanings death can take before an endpoint is observed. Covers when death blocks a time-to-event outcome, when it makes a later survivor-only outcome undefined, when it forces an estimand strategy, and how to stop those three problems from being blurred into one vague methods sentence.

Estimands Competing Risks Truncation by Death

Guide 53 — Causal Inference

Effect Modification vs Statistical Interaction in Clinical Research

A visual guide to the difference between clinical heterogeneity of treatment effect and the model-based interaction terms used to summarize it. Covers subgroup p-value traps, scale dependence, baseline-severity examples, and the reviewer checks that keep treatment-effect heterogeneity from collapsing into post hoc storytelling.

Subgroup Analysis Effect Heterogeneity Clinical Research

Guide 54 — Causal Inference

M-Bias and Clinical Severity Scores

A visual guide to one of the easiest adjustment mistakes to hide behind a validated score. Covers the difference between true confounding and collider-based M-bias, why severity composites can be causally unsafe, and the reviewer checks that keep “adjusted for severity” from masquerading as identification strategy.

Collider Bias Severity Scores Clinical Research

Guide 55 — Causal Inference

Regression to the Mean in Before-After Clinical Studies

A visual guide to why uncontrolled improvement after an extreme baseline can look like treatment benefit even when part of the change was expected all along. Covers threshold-triggered enrollment, noisy measurements, single-arm rescue therapy, and the design checks that keep before-after results from being oversold as causal evidence.

Before-After Studies Measurement Noise Clinical Research

Guide 56 — Causal Inference

Targeted Maximum Likelihood Estimation in Clinical Research

A visual guide to when TMLE earns its complexity in observational clinical studies. Covers the estimand-first workflow, why targeting is different from generic machine-learning adjustment, how TMLE compares with g-computation, IPW, and AIPW, and the reviewer checks that catch overlap, design, and reporting failures before the estimate gets oversold.

TMLE Doubly Robust Estimation Clinical Research

Guide 57 — Causal Inference

Case-Crossover Design in Clinical Research

A visual guide to the self-matched design for short-term trigger questions. Covers hazard and control windows, washout gaps, why stable confounding is reduced but time-varying confounding is not, and the reviewer checks that separate a real acute-trigger study from a chronic-treatment question wearing the wrong design.

Case-Crossover Self-Matched Design Clinical Research

Guide 58 — Causal Inference

Weak Instruments and Physician-Preference IVs in Clinical Research

A reviewer-focused guide to first-stage weakness, why physician- and hospital-preference instruments often proxy broader care systems, how local IV interpretation can drift, and the checks that should come before anyone claims unmeasured confounding is solved.

Instrumental Variables Weak Instruments Clinical Research

Guide 59 — Causal Inference

Stepped-Wedge Cluster Trials in Clinical Research

A rollout-focused guide to when stepped-wedge designs are actually useful, why calendar time is the central threat, and the reviewer checks that separate a defensible phased implementation trial from a dressed-up before-after study.

Cluster Trials Stepped-Wedge Design Clinical Research

Guide 60 — Causal Inference

Bias Amplification in Clinical Research

A clinically grounded guide to when adjustment worsens residual confounding, why strong treatment predictors are not automatically good covariates, and the reviewer checks that separate causal control from treatment-prediction theater.

Confounding Adjustment Strategy Clinical Research

Guide 61 — Causal Inference

Composite Endpoints in Clinical Causal Inference

A clinically grounded guide to when combining outcomes clarifies the estimand, when soft components overpower hard ones, and the reviewer checks that stop a statistically convenient composite from rewriting the clinical question.

Composite Endpoints Estimands Clinical Research

Guide 62 — Causal Inference

Healthy Adherer Bias in Clinical Research

A clinically grounded guide to when persistence starts to look like pharmacology, how adherence behavior gets tangled with prognosis and support, and the reviewer checks that keep refill discipline from masquerading as treatment effect.

Adherence Bias Observational Research Clinical Research

Guide 63 — Causal Inference

Grace Periods in Target Trial Emulation

A clinically grounded guide to when delayed initiation windows belong in the protocol, how hindsight exposure coding turns them into immortal time bias, and the reviewer checks that keep assignment windows aligned with a real target trial.

Target Trial Emulation Grace Periods Clinical Research

Guide 64 — Causal Inference

SMART Trials and Adaptive Treatment Strategies

A clinically grounded guide to when a treatment question is really a sequence of decisions, how SMART trials embed response-guided treatment regimes, and the reviewer checks that keep stage-wise randomization from being misread as a simple baseline drug comparison.

SMART Trials Adaptive Strategies Clinical Research

Guide 65 — Causal Inference

Nested Case-Control Design in Clinical Research

A clinically grounded guide to when expensive biomarker or chart-validated exposure data justify sampling inside a cohort, how risk-set control selection preserves the event-time comparison, and the reviewer checks that keep fake nesting from being mistaken for a real sampled-cohort design.

Nested Case-Control Risk-Set Sampling Clinical Research

Monotonicity, Compliers, and LATE in Clinical Instrumental-Variable Research Visual Guide

Guide 66 — Causal Inference

Monotonicity, Compliers, and LATE in Clinical IV Research

A clinically grounded guide to why most instrumental-variable estimates are local effects for compliers, how monotonicity becomes a real claim about treatment decisions moving in one direction, and the reviewer checks that keep LATE from being overstated as a population ATE.

Instrumental Variables LATE Clinical Research

Guide 67 — Causal Inference

Per-Protocol Effects in Pragmatic Trials

A clinically grounded guide to why per-protocol analyses in real-world trials become adherence and informative-deviation problems, when artificial censoring and IPCW enter, and the reviewer checks that keep randomization from being overstated after protocol drift begins.

Per-Protocol Pragmatic Trials Clinical Research

Guide 68 — Causal Inference

Interrupted Time Series in Clinical Policy Evaluation

A clinically grounded guide to how interrupted time series separates immediate level shifts from post-policy trend changes, why co-interventions and coding shocks can counterfeit causal stories, and the reviewer checks that make single-series policy evaluations more credible.

Interrupted Time Series Policy Evaluation Clinical Research

Guide 69 — Causal Inference

Transportability and Generalizability

A clinically grounded guide to why internal validity does not license the leap from "in this trial" to "in patients like ours," why the load-bearing quantities are effect modifiers rather than prognostic factors, how positivity of participation and scale (relative vs absolute) decide whether an effect actually transports, and the reviewer checks that separate a borrowed external claim from an earned one.

Transportability External Validity Clinical Research

Interactive prototype · Guide 69

Can this trial travel?

Randomization protects the comparison inside the trial. Change the target population and watch why that does not automatically identify the effect in patients like yours.

Randomized trialEligibility selected a lower-risk sample

Lower riskHigher risk

Target clinicModerate overlap

Lower riskEffect modifier present

Higher-risk patients in target 60%

Extra treatment benefit in higher-risk patients 8 pp

Trial estimate8.0% ARR

Naively exported estimate8.0% ARR

Standardized target estimate10.8% ARR

Same randomized trial, different population effect. Standardization gives more influence to the patients who are common in the target clinic.

Guide 70 — Causal Inference

Synthetic Control Methods

A clinically grounded guide to building a counterfactual when a policy or program hits a single unit — how synthetic control assembles a weighted twin from untreated donors that provably tracked the treated unit before the intervention, why the credibility lives in placebo and permutation inference rather than the visible gap, and the reviewer checks (pre-period fit, donor weights, spillover, anticipation) that separate an earned effect from a drawn one.

Synthetic Control Policy Evaluation Clinical Research

Guide 71 — Causal Inference

Partial Identification and Nonparametric Bounds

When the assumptions needed to name a single number aren't credible, don't invent the number — bound it. This guide shows how partial identification reports the full range of effects consistent with defensible assumptions instead of a fragile point: the humbling no-assumptions worst-case bound (width 1, always contains zero), the ladder of assumptions that narrows the interval one credible rung at a time (bounded outcome, monotone selection, monotone response, IV bounds), and how this differs from sensitivity analysis and IV point estimation. The antidote to false precision built on an undefended identifying assumption.

Partial Identification Nonparametric Bounds Clinical Research

Guide 72 — Causal Inference

Quantitative Bias Analysis

A confidence interval measures random error and quietly pretends systematic error is zero — which is why a study of two million patients can have a razor-thin CI around a badly biased number. This guide shows how quantitative bias analysis replaces the Discussion-section sentence "residual confounding cannot be excluded" with an actual interval: put an explicit, argued distribution on the bias — unmeasured confounding, misclassification, or selection — and propagate it through so the reported uncertainty finally includes the errors you lose sleep over. Covers the ladder from E-value to probabilistic Monte Carlo QBA, the six-step workflow, a worked anticoagulant-and-dementia vignette where the effect shifts back across the null, and a reviewer's checklist. The antidote to a "significant" result that rests entirely on the assumption of zero bias.

Quantitative Bias Analysis Probabilistic Bias Analysis Clinical Research

Guide 73 — Causal Inference

Staggered Difference-in-Differences

Real interventions rarely switch on everywhere at once — hospitals adopt a protocol in different quarters, states expand a policy in different years, a stepped-wedge trial crosses clusters on a schedule. Feed that staggered data to the standard two-way fixed-effects regression and it does something invisible and damaging: it uses already-treated units as controls for later-treated ones. That "forbidden comparison" lets a still-growing treatment effect leak into the counterfactual, producing negative weights that can shrink, inflate, or even flip the sign of your estimate — and more data does not fix it. This guide shows why the single β is not the ATT, how the Goodman-Bacon decomposition exposes the contamination, and the modern heterogeneity-robust estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille, imputation) that rebuild the analysis on clean controls only. Includes a worked ICU early-mobility vignette where a real benefit is buried as "not significant," the stepped-wedge connection, and a reviewer's checklist.

Difference-in-Differences Two-Way Fixed Effects Staggered Adoption

Guide 74 — Causal Inference

Mendelian Randomization

Higher HDL tracks with less heart disease; higher CRP with more; higher vitamin D with less of everything — and a distressing share of these associations are not causal. Mendelian randomization borrows the logic of a randomized trial from the one experiment nature already ran: meiosis. Because alleles are dealt at random at conception, a variant that raises a lifelong exposure acts like a treatment assignment, giving a confounding-resistant, reverse-causation-proof estimate of what that exposure actually does. This guide translates the three instrumental-variable assumptions into genetic terms, then spends its energy on the one that eats most MR studies — horizontal pleiotropy, the variant's side door to the outcome — and the robustness panel (IVW, MR-Egger, weighted median, MR-PRESSO) built to survive it. Includes the HDL and CRP cautionary tales where MR overturned the observational story before the trials read out, two-sample and winner's-curse hazards, weak-instrument and population-stratification traps, why an MR estimate is a lifelong effect and not a drug's effect size, and a STROBE-MR-aligned reviewer's checklist.

Instrumental Variables Genetic Epidemiology Pleiotropy

Guide 75 — Causal Inference

External Control Arms

When randomization is impossible — a rare disease, a dramatic effect, an accelerated-approval pathway — a single-arm trial supplies the treated patients and borrows its comparator from outside: a registry, an EHR, a historical trial. That borrowed control is a causal-inference problem in a trial costume, because no coin was ever flipped between the two groups. This guide is the patient-level sibling of the synthetic-control and target-trial guides, and it walks the four threats that decide whether the comparison survives — unmeasured confounding, eligibility mismatch, non-comparable endpoints, and calendar time drift — noting that nearly all of them push the same way: toward flattering the drug. It covers eligibility harmonization, time-zero alignment (and the immortal-time trap it prevents), propensity weighting for the treated effect, positivity/overlap, and Bayesian dynamic borrowing for hybrid designs; then the falsification toolkit an external control has to lean on — E-values, negative-control outcomes, tipping-point and multiple-source analyses — closing on the 2026 regulatory frame and a worked OS-vs-PFS vignette showing why the same hazard ratio means very different things depending on the endpoint. Includes 12 pitfalls and a pre-specification-first reviewer's checklist.

Single-Arm Trials Real-World Evidence Regulatory

Surrogate Endpoints and the Surrogate Paradox Visual Guide

Guide 76 — Causal Inference

Surrogate Endpoints & the Surrogate Paradox

Almost every fast drug approval rides on a surrogate — HbA1c for complications, LDL for infarction, tumor shrinkage for survival — a cheap, quick marker standing in for the slow outcome patients actually feel. But a surrogate is a causal promise: that the treatment's road to the outcome runs through the marker. When the drug has a second road — an off-target harm the marker never sees — you get the surrogate paradox: the marker improves and the patient gets worse. CAST suppressed ventricular ectopy and tripled deaths; torcetrapib raised HDL and killed more people; fluoride made bone denser and more fragile. This guide separates the two questions everyone conflates — is a marker prognostic (predicts outcome across patients) versus a valid surrogate (its treatment effect predicts the treatment effect on the outcome across trials) — and shows why a single study can never validate a surrogate. It walks the Prentice criteria and the fatal full-mediation condition, the meta-analytic trial-level R², principal surrogacy's link to principal stratification, why proportion-of-effect-explained is fragile, and a worked CKD/proteinuria vignette. Closes with the accelerated-approval frame, eight common mistakes, and a pre-specification-first reviewer's checklist.

Surrogate Endpoints Mediation Regulatory

Falsification Tests Design Family Visual Guide

Guide 77 — Causal Inference

Falsification Tests: The Design Family

Every observational claim rests on assumptions you cannot verify — no unmeasured confounding, parallel trends, a valid instrument. You can't prove them, but you can often derive a consequence that must hold if they're true, and check it. When that prediction is a mandatory zero — an effect on an outcome the drug can't cause, a "benefit" in the period before treatment began, a signal in a unit that was never treated — you've built a falsification test: a question you already know the answer to, posed to your data. A signal where none is allowed is a smoke alarm for a broken design. This guide teaches the family as one grammar rather than five disconnected tricks: negative-control outcomes and exposures, pre-period/parallel-trends placebo tests in difference-in-differences and interrupted time series, in-space permutation in synthetic control, future-outcome and anticipation checks, and the falsification tests built into instruments and Mendelian randomization. It shows the ladder from detect (one control) to bound (E-value) to correct (a proxy pair, via proximal causal inference), the logic trap that a passed test is necessary but never sufficient, why power and precision — not p-values — decide whether a "clean" control means anything, a worked healthy-user vignette, eight common mistakes, and a pre-specification-first reviewer's checklist.

Falsification Negative Controls Study Design

Guide 78 — Causal Inference

The Hazards of Hazard Ratios

The hazard ratio is the most-reported number in clinical survival research and one of the least understood. It compares instantaneous event rates among the people still at risk — so the moment a treatment changes who survives, it stops comparing the two groups you randomized and starts comparing two differently-selected subsets. This holds even in a perfect randomized trial. The guide separates three failures usually collapsed into one tidy number with a confidence interval: built-in selection (a rising late hazard ratio can be an artifact of who is left, not biology), non-collapsibility (the marginal ratio drifts toward the null over follow-up from frailty depletion alone), and non-proportional hazards (a single Cox estimate is a time-average whose weights depend on trial duration — so the same drug can report different ratios in trials of different length). It explains why a non-significant proportional-hazards test is the wrong reassurance, and what to report instead: survival curves with a number-at-risk table, absolute risk differences at pre-specified clinical times, and the restricted mean survival time (RMST) difference — a collapsible, assumption-light, patient-facing "average time gained." Includes a worked delayed-effect immuno-oncology vignette, eight common mistakes, and a curve-first reviewer's checklist.

Hazard Ratios Survival Analysis RMST

Guide 79 — Causal Inference

Recurrent Event Analysis

In many chronic diseases the burden is the repetition — heart-failure hospitalizations, COPD exacerbations, seizures, gout flares, bleeds. The reflex analysis, time-to-first-event, answers a smaller question and throws away everything after the first event: a drug that halves how often patients are hospitalized but doesn't delay the first one can look completely null in a Cox model. This guide teaches the recurrent-event family as one decision — state the estimand, then choose the model that targets it without lying about death. It separates the three things that make recurrent data hard: within-patient correlation (a variance problem, cheaply fixed with robust standard errors), a terminal event (death informatively stops the counting process — a more lethal arm accrues fewer events simply by leaving, and plain rate models flatter it), and over-dispersion (a few frequent-flyers). It maps the model menu to the question each one answers — Andersen–Gill and LWYY for burden, PWP for whether the effect wanes across successive events, negative-binomial for exacerbation counts, and Ghosh–Lin mean-cumulative-function or joint frailty models when death must be handled honestly — and closes on what to report: MCF curves by arm, a rate ratio with a robust interval, an absolute burden contrast, and an explicit death-accounting. Includes a worked heart-failure vignette where the same data read "no benefit" or "a third fewer hospitalizations" depending only on whether you count one event or all of them, eight common mistakes, and a burden-first reviewer's checklist.

Recurrent Events Survival Analysis Mean Cumulative Function

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