RMME Evaluation Colloquium

Behind the Evaluation: Holli Bayonas

Dr. Holli Bayonas
iEvaluate, LLC

Friday, November 19th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m83cfb05ec06ab0e6aecf026ad3e414f6

This colloquium gives participants an inside look at one evaluator’s pathway to becoming an evaluation professional. Dr. Bayonas will describe her personal career trajectory, along with the day-to-day responsibilities associated with her current position at iEvaluate. She will compare and contrast her opportunities to work in industry versus working for herself as an independent evaluation consultant. In addition, Dr. Bayonas will discuss her approach to balancing career/professional goals and the demands of homelife, including how she and her partner navigated the prioritization and support of each other’s career aspirations. She will close this talk with career and personal advice for her younger self.

Loading...

Taking too long?

Reload document

| Open in new tab

RMME/STAT Joint Colloquium

Overlap Weighting for Causal Inference

Dr. Fan Li
Duke University

Friday, October 1st, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=ma4999c9bf3ac28d40a9686eec33d70ed

Covariate balance is crucial for causal comparisons. Weighting is a common strategy to balance covariates in observational studies. We propose a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. Within the class, we highlight the overlap weighting method, which has been widely adopted in applied research. The overlap weight of each unit is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable exact balance property. Extension of overlap weighting to multiple treatments, survival outcomes, and subgroup analysis will also be discussed.

Loading...

Taking too long?

Reload document

| Open in new tab

RMME/STAT Joint Colloquium

Overlap Weighting for Causal Inference

Dr. Fan Li
Duke University

Friday, October 1st, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=ma4999c9bf3ac28d40a9686eec33d70ed

Covariate balance is crucial for causal comparisons. Weighting is a common strategy to balance covariates in observational studies. We propose a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. Within the class, we highlight the overlap weighting method, which has been widely adopted in applied research. The overlap weight of each unit is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable exact balance property. Extension of overlap weighting to multiple treatments, survival outcomes, and subgroup analysis will also be discussed.

Loading...

Taking too long?

Reload document

| Open in new tab

RMME/STAT Joint Colloquium

Assessing Personalization in Digital Health

Dr. Susan Murphy
Harvard University

Friday, September 10th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m883b79a16b8b2c21038a80da6301cba3

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? I discuss some first approaches to addressing these questions.

Loading...

Taking too long?

Reload document

| Open in new tab

RMME/STAT Joint Colloquium

Assessing Personalization in Digital Health

Dr. Susan Murphy
Harvard University

Friday, September 10th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m883b79a16b8b2c21038a80da6301cba3

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? I discuss some first approaches to addressing these questions.

Loading...

Taking too long?

Reload document

| Open in new tab