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Marginal Structural Models and Causal Inference in Epidemiology
In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also…
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- J. Robins
Double/Debiased Machine Learning for Treatment and Structural Parameters
This work revisits the classic semiparametric problem of inference on a low dimensional parameter θ_0 in the presence of high-dimensional nuisance parameters η_0 and proves that DML delivers point estimators that concentrate in a N^(-1/2)-neighborhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements.
Estimation of Regression Coefficients When Some Regressors are not Always Observed
Abstract In applied problems it is common to specify a model for the conditional mean of a response given a set of regressors. A subset of the regressors may be missing for some study subjects either…
Doubly Robust Estimation in Missing Data and Causal Inference Models
The results of simulation studies are presented which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict and the proposed method is applied to a cardiovascular clinical trial.
Identifiability and Exchangeability for Direct and Indirect Effects
It is shown that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased, and that, even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions.
Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.
The marginal structural Cox proportional hazards model is described and used to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study.
Unified Methods for Censored Longitudinal Data and Causality
A Unified Approach for Causal Inference andcensored Data is presented, which combines cross-sectional data and Right Censored Data Combined to form a single corpus ofcensored data.
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
It is estimated that a single infectious case of SARS will infect about three secondary cases in a population that has not yet instituted control measures, and public-health efforts to reduce transmission are expected to have a substantial impact on reducing the size of the epidemic.
Optimal Structural Nested Models for Optimal Sequential Decisions
- J. Robins
- Computer Science, Mathematics
A novel “Bayes-frequentist compromise” is proposed that combines honest subjective non- or semiparametric Bayesian inference with good frequentist behavior, even in cases where the model is so large and the likelihood function so complex that standard Bayes procedures have poor frequentist performance.