8 papers
When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition
M. Ehsan Karim, Rainie Fu
Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations trai…
When bad adjustment looks good: what goes wrong in Plasmode 0.1.0 simulations
M. Ehsan Karim
Comparisons of confounding-control methods are informative only when simulated data contain the treatment-covariate relationship those methods address. We assessed whether Plasmode…
A Plasmode Benchmark Can Refute but Cannot Certify: Projection and Regularity in Causal-Inference Simulation
M. Ehsan Karim
Plasmode simulation - resampling covariates, and often treatment, from a real cohort and regenerating the outcome from a model with an injected effect - is a standard tool for comp…
Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers
M. Ehsan Karim
Augmented inverse-probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three routes to the same efficient inf…
Best for which estimand? A known-truth benchmark of longitudinal-matching and target-trial-emulation methods for time-varying treatments
M. Ehsan Karim
On a non-collapsible survival mechanism, longitudinal-matching and target-trial-emulation methods are not competing estimators of one truth but answers to different causal question…
Cluster on the Subject, Not the Record: Confidence Intervals and Simultaneous Bands for Additive-Hazards Sequential Trial Emulation
M. Ehsan Karim
Sequential trial emulation (STE) estimates the effect of a sustained treatment by stacking nested emulated trials with inverse-probability weighting. Additive-hazards STE estimator…
When Does Trial-Real-World Data Fusion Improve Precision? Model Auditing and Selection-Aware Inference for Adaptive-TMLE
M. Ehsan Karim
Augmenting a randomized controlled trial (RCT) with real-world data (RWD) promises greater efficiency, but how much a given fusion delivers, and how to attach honest uncertainty to…
Cross-Fitted Survey-Weighted TMLE with Design-Based Variance for Causal Machine Learning
M. Ehsan Karim
Cross-fitting is not a refinement of survey-weighted causal machine learning but, once the nuisances are flexible, what restores valid inference. We study the population average tr…