Establishes unconfoundedness conditions and proposes matching-based estimators for immediate and carryover causal effects in bipartite interference with time series and random networks.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
2 Pith papers cite this work. Polarity classification is still indexing.
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2024 2verdicts
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A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.
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Bipartite causal inference with interference, time series data, and a random network
Establishes unconfoundedness conditions and proposes matching-based estimators for immediate and carryover causal effects in bipartite interference with time series and random networks.
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Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
A stability-derived update formula for a generalized debiased Lasso yields asymptotically accurate approximations for most coordinates under sub-Gaussian designs in the proportional regime, enabling faster resampling-based variable selection.