3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Sensitivity analysis for transportability in multi-study, multi-outcome settings
Ngoc Q. Duong, Amy J. Pitts, Soohyun Kim +1
Existing work in data fusion has covered identification of causal estimands when integrating data from heterogeneous sources. These results typically require additional assumptions…
All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples
Kara E. Rudolph, Nicholas Williams, Caleb H. Miles +2
There is a long-standing debate in the statistical, epidemiological and econometric fields as to whether nonparametric estimation that uses data-adaptive methods, like machine lear…
On the Causal Interpretation of Randomized Interventional Indirect Effects
Caleb H. Miles
Identification of standard mediated effects such as the natural indirect effect relies on heavy causal assumptions. By circumventing such assumptions, so-called randomized interven…
Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed
Caleb H. Miles, Maya Petersen, Mark J. van der Laan
The assumption that no subject's exposure affects another subject's outcome, known as the no-interference assumption, has long held a foundational position in the study of causal i…
A Class of Semiparametric Tests of Treatment Effect Robust to Confounder Classical Measurement Error
Caleb H. Miles, Joel Schwartz, Eric J. Tchetgen Tchetgen
When assessing the presence of an exposure causal effect on a given outcome, it is well known that classical measurement error of the exposure can reduce the power of a test of the…
On Partial Identification of the Pure Direct Effect
Caleb H. Miles, Phyllis Kanki, Seema Meloni +1
In causal mediation analysis, nonparametric identification of the pure (natural) direct effect typically relies on, in addition to no unobserved pre-exposure confounding, fundament…