arXiv subjects

J. M. Robins

Publications and source records attributed to J. M. Robins.

3 recordsLinked to original sources

arXiv2023

Comment on "Examining the effect of counter-narratives about physics on women's physics career intentions"

A paper evaluating the effects of lessons intended to encourage high school students to continue physics studies made some important errors. One was to underestimate the width of confidence intervals by failing to use standard cluster randomization analysis. Another was to use a missing-data imputation program that inappropriately assumes that data are missing at random, leading to potential bias in estimating the effect. The last was to omit discussion of how the treatment used was likely to produce substantial social desirability survey response bias, eroding external validity.

physics.ed-ph↗

arXiv2023

Causal Inference for Complex Longitudinal Data: The Continuous Time g-Computation Formula

We extend Robins' theory of causal inference for complex longitudinal data to the case of continuously varying as opposed to discrete covariates and treatments. In particular we establish versions of the key results of the discrete theory: the g-computation formula and a collection of powerful characterizations of the g-null hypothesis of no treatment effect. This is accomplished under natural continuity hypotheses concerning the conditional distributions of the outcome variable and of the covariates given the past. We also show that our assumptions concerning counterfactual variables place no restriction on the joint distribution of the observed variables: thus in a precise sense, these assumptions are "for free," or if you prefer, harmless.

math.ST↗

arXiv2004

Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models.

math.ST↗