38 citations · 38 across the 3 of their papers we have counts for
Showing stat.MEShow all
2 papers · 1 filter
stat.ME2026
Parallel Trends as a Spectrum
Kyle Schindl, Joseph Fry, Florian Gunsilius +1
Difference-in-differences (DiD) designs are typically used to identify and estimate average causal effects, even though treatment can affect the entire outcome distribution. This h…
stat.ME2024
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…