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Fan Li, PhD

Associate Professor, Duke University Department of Statistical Science

Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. We consider weighting strategies for balancing covariates. We define 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. General large-sample results on nonparametric estimation based on these weights are derived. We further propose a new weighting scheme, the overlap weights, in which each unit's weight 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 small-sample exact balance property, based on which we propose a new method that achieves exact balance for means of any selected set of covariates. Extensions to multiple treatments and subgroup analysis will also be discussed.

### MRBIII, Room 1220

12 February 2020

1:30pm

12 February 2020

1:30pm

Speaker Itinerary

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Topic revision: r1 - 14 Jan 2020, TawannaPeters

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