273 citations · 856 across the 22 of their papers we have counts for
15 papers · 1 filter
Bias and sensitivity analysis for unmeasured confounders in linear structural equation models
Adam J. Sullivan, Tyler J. VanderWeele
In this paper, we consider the extent of the biases that may arise when an unmeasured confounder is omitted from a structural equation model (SEM) and we propose sensitivity analys…
Longitudinal Mediation Analysis with Latent Growth Curves
Adam J. Sullivan, Douglas D. Gunzler, Nathan Morris +1
The paper considers mediation analysis with longitudinal data under latent growth curve models within a counterfactual framework. Estimators and their standard errors are derived f…
Constructed measures and causal inference: towards a new model of measurement for psychosocial constructs
Tyler J. VanderWeele
Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective an…
A statistical test to reject the structural interpretation of a latent factor model
Tyler J. VanderWeele, Stijn Vansteelandt
Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying co…
Multiple-bias sensitivity analysis using bounds
Louisa H. Smith, Maya B. Mathur, Tyler J. VanderWeele
Unmeasured confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitation…
On the dimensional indeterminacy of one-wave factor analysis under causal effects
Tyler J. VanderWeele, Charles J. K. Batty
It is shown, with two sets of indicators that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one…
Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time
Jaffer M. Zaidi, Eric J. Tchetgen Tchetgen, Tyler J. VanderWeele
The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics and causal inference. The survivor average causal effect…
Simple Sensitivity Analysis for Differential Measurement Error
Tyler J. VanderWeele, Yige Li
Simple sensitivity analysis results are given for differential measurement error of either the exposure or the outcome. In the case of differential measurement error of the outcome…
Outcome-wide longitudinal designs for causal inference: a new template for empirical studies
Tyler J. VanderWeele, Maya B. Mathur, Ying Chen
In this paper we propose a new template for empirical studies intended to assess causal effects: the outcome-wide longitudinal design. The approach is an extension of what is often…
Bounding bias due to selection
Louisa H. Smith, Tyler J. VanderWeele
When epidemiologic studies are conducted in a subset of the population, selection bias can threaten the validity of causal inference. This bias can occur whether or not that select…
Selecting optimal subgroups for treatment using many covariates
Tyler J. VanderWeele, Alex R. Luedtke, Mark J. van der Laan +1
We consider the problem of selecting the optimal subgroup to treat when data on covariates is available from a randomized trial or observational study. We distinguish between four…
Vaccines, Contagion, and Social Networks
Elizabeth L. Ogburn, Tyler J. VanderWeele
Consider the causal effect that one individual's treatment may have on another individual's outcome when the outcome is contagious, with specific application to the effect of vacci…
Causal Diagrams for Interference
Elizabeth L. Ogburn, Tyler J. VanderWeele
The term "interference" has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among thre…
On the definition of a confounder
Tyler J. VanderWeele, Ilya Shpitser
The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The literature has not, however, come to an…
On the Validity of Covariate Adjustment for Estimating Causal Effects
Ilya Shpitser, Tyler VanderWeele, James M. Robins
Identifying effects of actions (treatments) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference. This identification is…