Causal Inference
Everything on Aqrab tagged Causal Inference — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
Treatment-Induced Mediator-Outcome Confounding: When Mediation Analysis Starts Chasing the Consequences of Treatment
A practical guide to treatment-induced mediator-outcome confounding for clinical researchers. Covers why natural direct and indirect effects fail when treatment changes later severity, toxicity, adherence, or surveillance that affect both the mediator and outcome.
Stochastic Interventions: When “Treat Everyone” Is Not the Policy Question
A practical guide to stochastic interventions for clinical researchers. Covers when deterministic treatment rules become unrealistic, how probability-shift interventions preserve positivity, and what reviewers should demand before trusting policy-effect claims.
Calendar Time Confounding: When Secular Trends Pretend Your Intervention Worked
A practical guide to calendar time confounding for clinical researchers. Covers secular trends, treatment diffusion, concurrent comparators, and what reviewers should demand before trusting real-world benefit that may just reflect a later era.
Prevalent-User Bias: When Your Drug Study Starts After the Interesting Harm Already Happened
A practical guide to prevalent-user bias for clinical researchers. Covers depletion of susceptibles, survivor selection, post-treatment baseline covariates, and what reviewers should demand before trusting late-entry treatment cohorts.
Clone-Censor-Weight: The Target Trial Fix That Still Breaks When You Use It Casually
A practical guide to clone-censor-weight for clinical researchers. Covers when the design is needed, how cloning and artificial censoring work, where immortal time bias reappears, and what reviewers should demand before trusting a target trial emulation.
Noncollapsibility of Odds Ratios: Why Adjustment Can Change the Number Even When Confounding Did Not
A practical guide to noncollapsibility of odds ratios for clinical researchers. Covers why crude and adjusted odds ratios can differ without confounding, when logistic regression invites over-interpretation, and what reviewers should demand instead.
Composite Endpoints: When One Trial Outcome Quietly Becomes Four Different Clinical Questions
A practical guide to composite endpoints for clinical researchers. Covers when endpoint bundles improve efficiency, when they distort clinical meaning, how soft components hijack results, and what reviewers should demand before trusting the headline.
Per-Protocol Effects: The Estimand Everyone Wants and the Bias Trap They Usually Build
A practical guide to per-protocol effects for clinical researchers. Covers sustained-adherence estimands, naive as-treated failure, selection bias after protocol deviation, and what reviewers should demand before trusting per-protocol claims.
Prediction vs Causation: Why Your Best Risk Model Still Cannot Tell You What to Treat
A practical guide for clinical researchers on the difference between prediction and causation. Covers why strong risk models do not identify treatment effects, how to frame the right estimand, and what reviewers should flag in AI-driven clinical studies.
Restricted Mean Survival Time: When Hazard Ratios Are Not the Clinical Answer
A practical guide to restricted mean survival time for clinical researchers. Covers what RMST estimates, when it beats the hazard ratio, how to choose the time horizon, and how to report results clinicians can actually interpret.
Landmark Analysis: Useful, Honest, and Frequently Overclaimed
A practical guide to landmark analysis for clinical researchers. Covers delayed treatment, immortal time bias, conditional survivor populations, landmark selection, and why a cleaner timeline still changes the causal question.
Targeted Maximum Likelihood Estimation: Doubly Robust, Not Doubly Forgiving
A practical guide to targeted maximum likelihood estimation for clinical researchers. Covers nuisance models, clever covariates, machine learning, overlap diagnostics, and why TMLE is robust in theory but never permission to stop thinking.
Case-Crossover Design: When Patients Become Their Own Controls
A practical guide to case-crossover designs for clinical researchers. Covers self-matching, hazard versus control windows, transient exposures, protopathic bias, time trends, and when this elegant design is exactly right or exactly wrong.
Multiple Imputation: Missing Data Does Not Become Innocent Because MICE Ran
A practical guide to multiple imputation for clinical researchers. Covers MICE, complete-case failure, MAR versus MNAR, imputation-model design, and why missing data needs causal thinking instead of software ritual.
G-Computation: Predict the Outcome Under Each Treatment Strategy
A practical guide to g-computation for clinical researchers. Covers counterfactual prediction, standardization, outcome modeling, positivity, model misspecification, and how to estimate causal effects by averaging predicted outcomes under competing interventions.
Estimands: The Causal Question You Should Define Before Running the Analysis
A practical guide to estimands for clinical researchers. Covers treatment strategies, intercurrent events, target populations, summary measures, and why many studies fail because they never define the actual causal question clearly.
Time-Varying Confounding: When Yesterday's Treatment Changes Today's Confounder
A practical guide to time-varying confounding for clinical researchers. Covers treatment-confounder feedback, why ordinary regression fails, and how MSMs, g-methods, and target trial logic handle evolving treatment decisions.
Informative Censoring: When Dropout Is Part of the Bias
A practical guide to informative censoring for clinical researchers. Covers loss to follow-up, treatment discontinuation, database exit, inverse probability of censoring weights, and why dropout can bias survival and causal estimates when it depends on prognosis.
Self-Controlled Case Series: When Each Patient Becomes Their Own Control
A practical guide to self-controlled case series for clinical researchers. Covers transient exposures, acute outcomes, fixed-confounding control, event-dependent exposure, and why within-person designs still live or die on timing assumptions.
Measurement Error: When Bad Variables Break Good Causal Methods
A practical guide to measurement error for clinical researchers. Covers noisy exposures, weak confounder proxies, surveillance-driven outcomes, validation strategies, and why sophisticated causal methods cannot rescue bad variables.
Competing Risks: When Kaplan-Meier Tells the Wrong Clinical Story
A practical guide to competing risks for clinical researchers. Covers death and discharge as competing events, why Kaplan-Meier can overstate event probability, and how cause-specific hazards and cumulative incidence answer different clinical questions.
Bias Amplification: When Adjustment Makes Unmeasured Confounding Worse
A practical guide to bias amplification for clinical researchers. Covers near-instruments, noisy severity proxies, treatment-prediction traps, and why the wrong adjustment variable can magnify residual confounding instead of reducing it.
Misclassification Bias: When Your Variables Lie Before the Model Starts
A practical guide to misclassification bias for clinical researchers. Covers wrong exposure and outcome labels, weakly measured confounders, surveillance-driven event detection, and why bad variables can distort causal estimates before modeling even begins.
Active Comparator New-User Design: The Observational Study Upgrade Most Drug Papers Need
A practical guide to the active comparator new-user design for clinical researchers. Covers why treated-versus-untreated comparisons fail, how new-user cohorts reduce prevalent-user bias, how active comparators narrow confounding by indication, and what reviewers should demand before trusting comparative effectiveness claims.
Selection Bias: When Your Study Sample Is the Problem
A practical guide to selection bias for clinical researchers. Covers referral filtering, survivor bias, complete-case analysis, informative loss to follow-up, collider-driven selection, and why a clean model cannot rescue a distorted sample.
Confounding by Indication: When Sicker Patients Make Treatments Look Dangerous
A practical guide to confounding by indication for clinical researchers. Covers treatment selection, severity-driven prescribing, contraindication bias, why routine adjustment often fails, and how to design observational comparisons that do not confuse prognosis with treatment effect.
Immortal Time Bias: The Fake Survival Advantage Hiding in Bad Study Design
A practical guide to immortal time bias for clinical researchers. Covers time zero, future-based exposure definitions, delayed treatment initiation, target trial emulation, and why you cannot adjust your way out of a broken timeline.
Interrupted Time Series: Strong Quasi-Experiments Need More Than a Before-and-After Plot
A practical guide to interrupted time series for clinical researchers. Covers level and slope changes, segmented regression, seasonality, autocorrelation, controlled ITS designs, and why a vertical line on a chart is not a causal estimate.
Parametric G-Formula: Estimating Causal Effects When Covariates Change Over Time
A practical guide to the parametric g-formula for clinical researchers. Covers time-varying confounding, dynamic treatment strategies, longitudinal simulation, model diagnostics, and why ordinary regression breaks when covariates are changed by prior treatment.
Proximal Causal Inference: What to Do When Unmeasured Confounding Is Still on the Table
A practical guide to proximal causal inference for clinical researchers. Covers proxy variables, treatment-inducing versus outcome-inducing proxies, bridge functions, completeness, and why this method is powerful but brutally assumption-heavy.
Positivity & Overlap: The Assumption Your Causal Estimate Cannot Survive Without
A practical guide to positivity and overlap for clinical researchers. Covers common support, extreme weights, trimming, overlap-focused estimands, and why many causal analyses fail because treated and untreated patients barely resemble each other.
Transportability & External Validity: When Your Causal Estimate Travels, and When It Absolutely Does Not
A practical guide to transportability and external validity for clinical researchers. Covers target populations, effect heterogeneity, trial selection, overlap, reweighting, and why “generalizable” is usually a lazy claim unless you prove the estimate can actually travel.
Collider Bias: How Adjustment Can Manufacture Associations
A practical guide to collider bias for clinical researchers. Covers common-effect conditioning, Berkson bias, selected cohorts, complete-case traps, and why the wrong adjustment set can literally manufacture a result.
Interference & Spillover Effects: When One Patient's Treatment Changes Another's Outcome
A practical guide to interference and spillover effects for clinical researchers. Covers SUTVA violations, direct versus indirect effects, partial interference, cluster and network designs, and why contamination is often the estimand trying to get your attention.
Principal Stratification: Estimating Effects When Post-Treatment Variables Matter
A practical guide to principal stratification for clinical researchers. Covers compliers, always-takers, truncation by death, latent strata, CACE/LATE, and why conditioning on observed post-treatment subgroups is usually causal self-sabotage.
Overadjustment Bias: When More Covariates Make Causal Inference Worse
A practical guide to overadjustment bias for clinical researchers. Covers mediators, colliders, post-treatment variables, propensity score misuse, and why the biggest adjustment set is often the least credible one.
Front-Door Criterion: The Causal Backdoor Alternative Nobody Uses Enough
A practical guide to the front-door criterion for causal inference. Covers full mediation, mediator-outcome confounding, identification logic, DAG requirements, and why most real datasets are nowhere near clean enough for a credible front-door design.
Negative Controls: The Bias Check Most Observational Studies Skip
A practical guide to negative control outcomes and exposures for clinical researchers. Covers residual confounding, selection bias, surveillance bias, falsification endpoints, and how to interpret a failed negative control without lying to yourself.
DAG Construction: How to Draw a Causal Graph Before You Touch the Model
A practical guide to DAG construction for clinical researchers. Covers time ordering, node selection, confounders vs mediators vs colliders, minimally sufficient adjustment sets, and why most covariate lists are just causal confusion wearing a regression badge.
Mediation Analysis: When You Want the Mechanism, Not Just the Effect
A practical guide to mediation analysis for clinical researchers. Covers direct and indirect effects, mediator-outcome confounding, treatment-induced confounding, interventional effects, and why most mediator-adjusted regressions are wrong.
G-Estimation: The Causal Method You Reach For When Time-Varying Confounding Breaks Regression
A practical guide to g-estimation and structural nested models for clinical researchers. Covers treatment-confounder feedback, blipped-down outcomes, identifying assumptions, and when g-estimation beats naive longitudinal regression or unstable weights.
Causal Forests: Finding Treatment Effect Heterogeneity Without Fooling Yourself
A practical guide to causal forests for estimating who benefits more, less, or not at all. Covers CATEs, honest splitting, overlap, validation, clinical use cases, and the reporting standards reviewers should expect.
E-values & Sensitivity Analysis: How to Stress-Test Causal Claims
A practical guide to the one question every observational study must answer: how strong would an unmeasured confounder have to be to erase your result? Covers E-values, confidence-limit interpretation, Rosenbaum bounds, negative controls, and the reporting language reviewers trust.
Mendelian Randomization: Using Genetics as Nature's Randomized Trial
How genetic variants serve as natural instruments for causal inference — and why horizontal pleiotropy, population stratification, and weak instruments break most published MR studies. Covers two-sample MR, MR-Egger, MR-PRESSO, and the STROBE-MR reporting checklist.
Marginal Structural Models: A Practical Guide for Clinical Researchers
How MSMs use stabilized inverse probability weights to handle time-varying confounders — the ones that change over time and are affected by prior treatment. Covers weight estimation, model fitting, clinical examples, and common pitfalls.
Structural Causal Models & DAGs: A Practical Guide for Clinical Researchers
The causal framework behind every method you use. Covers DAGs, d-separation, do-calculus, backdoor/frontdoor criteria, mediation analysis, and how to draw the graph that makes your analysis work.
Inverse Probability Weighting: When PSM Discards Your Data
Why IPW outperforms matching by keeping all patients — and how extreme weights, positivity violations, and wrong variance estimators break published analyses silently.
Double Machine Learning: A Practical Guide for Clinical Researchers
How DML uses machine learning to estimate causal effects while controlling for high-dimensional confounders. Covers cross-fitting, Neyman orthogonality, clinical applications, and implementation in EconML.
Target Trial Emulation: A Practical Guide for Clinical Researchers
The framework that bridges observational data and causal claims — by asking what RCT you wish you had. Covers protocol specification, time zero alignment, clone-censor-weight, immortal time bias, and reporting.
Regression Discontinuity Design: A Practical Guide for Clinical Researchers
RDD turns arbitrary thresholds into causal evidence. Covers sharp vs fuzzy designs, bandwidth selection, manipulation testing, clinical applications, and a complete reporting checklist.
Synthetic Control Methods: Building Counterfactuals When DID Fails
How to construct a synthetic twin from donor pools when parallel trends don't hold. Covers SCM optimization, validation via placebo tests, modern extensions (ASCM, SDID), and common pitfalls.
Difference-in-Differences: A Practical Guide for Clinical Researchers
When and how to use DID in clinical research. Covers parallel trends, staggered adoption, common pitfalls, reporting checklist, and modern estimators.
Instrumental Variables: When Observational Data Meets Unmeasured Confounding
When PSM and regression fail because of unmeasured confounding, IV methods offer a way forward. A practical guide covering instruments, LATE, Mendelian randomization, and the exclusion restriction.
Propensity Score Matching: A Practical Guide for Clinical Researchers
What PSM actually does, when it fails, and how to report it correctly. Written for researchers who want to use it — not just cite it.
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