15 citations · 21 across the 11 of their papers we have counts for
9 papers · 1 filter
Improved bounds and inference on optimal regimes
Julien D. Laurendeau, Aaron L. Sarvet, Mats J. Stensrud
Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, informative bounds on these effects can often be deriv…
Optimal regimes with limited resources
Aaron L. Sarvet, Julien D. Laurendeau, Mats J. Stensrud
Policy-makers are often faced with the task of distributing a limited supply of resources. To support decision-making in these settings, statisticians are confronted with two chall…
Interpretational errors in statistical causal inference
Aaron L. Sarvet, Mats J. Stensrud, Lan Wen
We formalize an interpretational error that is common in statistical causal inference, termed identity slippage. This formalism is used to describe historically-recognized fallacie…
Separable effects for adherence
Kerollos Nashat Wanis, Mats Julius Stensrud, Aaron Leor Sarvet
Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as i…
Causal effects of intervening variables in settings with unmeasured confounding
Lan Wen, Aaron L. Sarvet, Mats J. Stensrud
We present new results on average causal effects in settings with unmeasured exposure-outcome confounding. Our results are motivated by a class of estimands, e.g., frequently of in…
Grace periods in comparative effectiveness studies of sustained treatments
Kerollos Nashat Wanis, Aaron L. Sarvet, Lan Wen +4
Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging f…
Optimal regimes for algorithm-assisted human decision-making
Mats J. Stensrud, Julien Laurendeau, Aaron L. Sarvet
We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatme…
Conditional separable effects
Mats J. Stensrud, James M. Robins, Aaron Sarvet +2
Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of diffe…
Causal inference with limited resources: proportionally-representative interventions
Aaron L. Sarvet, Kerollos N. Wanis, Jessica Young +3
Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment…