Social scientists use conjoint analysis, which is based on randomized experiments with a factorial design, to analyze multidimensional preferences in a population. In such experiments, several factors, each with multiple levels, are randomized to form a large number of possible treatment conditions. To explore causal interaction in factorial exp...
Creator:
Imai, Kosuke (Princeton University)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
There is effect modification if the magnitude or stability of a treatment effect varies systematically with the level of an observed covariate. A larger or more stable treatment effect is typically less sensitive to bias from unmeasured covariates, so it is important to recognize effect modification when it is present. Additionally, effect modif...
Creator:
Small, Dylan (University of Pennsylvania)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, I will introduce doubly-robust G-estimation of an adaptive treatment strategy in which parameters are shared across different stages of the treatment sequence, allowing for more efficient estimation and simpler treatment decision rules. The approach is computationally stable, and produces consistent estimators provided either the o...
Creator:
Moodie, Erica (McGill University)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
There has been considerable interest across several fields in methods that reduce the problem of learning good treatment assignment policies to the problem of accurate policy evaluation. Given a class of candidate policies, these methods first effectively evaluate each policy individually, and then learn a policy by optimizing the estimated valu...
Creator:
Wager, Stefan (Stanford University)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Statistical inferences from multiple data sources can often be fused together to yield more effective inference than from individual source alone. Such fusion learning is of vital importance for big data where data are often assembled in various domains. This paper develops a fusion methodology called individualized fusion learning (iFusion), to...
Creator:
Xie, Minge (Rutgers, The State University Of New Jersey)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we review several new developments in outcome weighted learning for identifying individualized treatment rules for two challenging settings: dose finding and treatment selection under right censoring. In the former, we develop...
Creator:
Kosorok, Michael (University of North Carolina, Chapel Hill)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Large medical data collected from clinical trials and observational studies often exhibit heterogeneity due to various reasons, such as difference in geographical locations as commonly seen in multi-center studies. Due to the heterogeneity in data, the optimal treatment decision might vary across patients from different study populations. As suc...
Creator:
Lu, Wenbin (North Carolina State University)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Propensity scores (Rosenbaum and Rubin, 1983) are used widely to address measured confounding in quasiexperiments. They also arise in connection with the antecedent question of whether non-equivalent treatment and control groups are suitable for comparison at all, with or without covariate adjustments.  "Common support,†the assumption that ...
Creator:
Hansen, Ben (University of Michigan)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In practice of medicine, multiple treatments are often available to treat individual patients. The task of identifying the best treatment for a specific patient is very challenging due to patient inhomogeneity. Multi-armed bandit with covariates provides a framework for designing effective treatment allocation rules in a way that integrates the ...
Creator:
Yang, Yuhong (University of Minnesota, Twin Cities)
Created:
2017-09-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
