RAMEN identifies treatment effects from multiple environments in a doubly robust manner by leveraging data heterogeneity without requiring the causal graph.
Dimension-agnostic inference using cross U-statistics
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A new hypothesis test and asymptotic lower bound detect maximum subgroup-level treatment effect bias when benchmarking observational studies against RCTs.
citing papers explorer
-
Doubly robust identification of treatment effects from multiple environments
RAMEN identifies treatment effects from multiple environments in a doubly robust manner by leveraging data heterogeneity without requiring the causal graph.
-
Detecting critical treatment effect bias in small subgroups
A new hypothesis test and asymptotic lower bound detect maximum subgroup-level treatment effect bias when benchmarking observational studies against RCTs.