108 citations · 355 across the 29 of their papers we have counts for
18 papers · 1 filter
Adaptive Orthogonalization for Stable Estimation of the Effects of Time-Varying Treatments
Yige Li, María de los Angeles Resa, José R. Zubizarreta
Inferring the causal effects of time-varying treatments is often hindered by highly variable inverse propensity weights, particularly in settings with limited covariate overlap. Bu…
Design-based inference for generalized network experiments with stochastic interventions
Ambarish Chattopadhyay, Kosuke Imai, Jose R. Zubizarreta
A growing number of researchers are conducting randomized experiments to analyze causal relationships in network settings where units influence one another. A dominant methodology…
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes
Kwangho Kim, Bijan A. Niknam, José R. Zubizarreta
Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the wei…
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Kwangho Kim, José R. Zubizarreta
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show…
Notes on Causation, Comparison, and Regression
Ambarish Chattopadhyay, Jose R. Zubizarreta
Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infea…
lmw: Linear Model Weights for Causal Inference
Ambarish Chattopadhyay, Noah Greifer, Jose R. Zubizarreta
The linear regression model is widely used in the biomedical and social sciences as well as in policy and business research to adjust for covariates and estimate the average effect…