56 citations · 128 across the 10 of their papers we have counts for
11 papers
Long-term Causal Inference Under Persistent Confounding via Data Combination
Guido Imbens, Nathan Kallus, Xiaojie Mao +1
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only…
Rerandomization with Diminishing Covariate Imbalance and Diverging Number of Covariates
Yuhao Wang, Xinran Li
Completely randomized experiments have been the gold standard for drawing causal inference because they can balance all potential confounding on average. However, they may suffer f…
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders
Yuhao Wang, Rajen D. Shah
We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some f…
Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques +2
Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated…
Permutation-Based Causal Structure Learning with Unknown Intervention Targets
Chandler Squires, Yuhao Wang, Caroline Uhler
We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This…
Anchored Causal Inference in the Presence of Measurement Error
Basil Saeed, Anastasiya Belyaeva, Yuhao Wang +1
We consider the problem of learning a causal graph in the presence of measurement error. This setting is for example common in genomics, where gene expression is corrupted through…