1.6k citations · 1.7k across the 23 of their papers we have counts for
25 papers
Minimax optimal subgroup identification
Matteo Bonvini, Edward H. Kennedy, Luke J. Keele
Quantifying treatment effect heterogeneity is a crucial task in many areas of causal inference, e.g. optimal treatment allocation and estimation of subgroup effects. We study the p…
Approximate Balancing Weights for Clustered Observational Study Designs
Luke Keele, Eli Ben-Michael, Lindsay Page
In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustmen…
Improving instrumental variable estimators with post-stratification
Nicole E. Pashley, Luke Keele, Luke W. Miratrix
Experiments studying get-out-the-vote (GOTV) efforts estimate the causal effect of various mobilization efforts on voter turnout. However, there is often substantial noncompliance…
A Guide to Regression Discontinuity Designs in Medical Applications
Matias D. Cattaneo, Luke Keele, Rocio Titiunik
We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands with…
Covariate-assisted bounds on causal effects with instrumental variables
Alexander W. Levis, Matteo Bonvini, Zhenghao Zeng +2
When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the…
Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor
Eli Ben-Michael, Luke Keele
Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers can typically focus on either the average treatment e…