Randomization Inference When N Equals One
A statistical theory for N-of-1 experiments, where a unit serves as its own control and treatment in rapid interleaving time windows.
CausalInference
A Convexified Matching Approach to Imputation and Individualized Inference
We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport.
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
Confounding can obfuscate the definition of the best prediction model (concept shift) and shift covariates to domains yet unseen (covariate shift). Therefore, a model maximizing prediction accuracy in the source environment could suffer a significant accuracy drop in the target environment. We propose a new domain adaptation method for observational data in the presence of confounding, and characterize the the stability and predictability tradeoff leveraging a structural causal model.
Deep Neural Networks for Estimation and Inference
Can deep neural networks with standard archtectures estimate treatment effects and perform downstream uncertainty quantification tasks?