11 citations · 12 across the 3 of their papers we have counts for
5 papers · 1 filter
A Fast Bootstrap Algorithm for Causal Inference with Large Data
Matthew Kosko, Lin Wang, Michele Santacatterina
Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful…
Robust weights that optimally balance confounders for estimating marginal hazard ratios
Michele Santacatterina
Covariate balance is crucial in obtaining unbiased estimates of treatment effects in observational studies. Methods based on inverse probability weights have been widely used to es…
Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
Nathan Kallus, Michele Santacatterina
Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most c…
Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching
Nathan Kallus, Michele Santacatterina
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted aver…
Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models
Nathan Kallus, Michele Santacatterina
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approac…