2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random
Lorenzo Testa, Edward H. Kennedy, Matthew Reimherr
The Difference-in-Differences (DiD) method is a fundamental tool for causal inference, yet its application is often complicated by missing data. Although recent work has developed…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
Hierarchical and Density-based Causal Clustering
Kwangho Kim, Jisu Kim, Larry A. Wasserman +1
Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating heterogeneous treatment effects pose significant chall…
Causal K-Means Clustering
Kwangho Kim, Jisu Kim, Edward H. Kennedy
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since t…