Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly the representations with invariant marginal distribution of input features, however, invariance conditional labels given features is more essential for unknown domain prediction. Meanwhile, existing unobserved confounders which affect and si...

Instrumental Variables are a popular way to identify the direct causal effect of a random variable X on a variable Y . Often no single instrumental variable exists, although it is still possible to find a set of generalized instrumental variables (GIVs) and identify the causal effect of all these variables at once. Till now it was not known how to find GIVs systematically or even test efficient...

The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen (2005)) is a popular tool for estimating causal effects with endogenous covariates. However, estimation complicated by the nonsmoothness nonconvexity of IVQR GMM objective function. This paper shows that problem can be decomposed into set conventional subproblems which are convex solved efficiently. reformulatio...

Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confound...

BACKGROUND Evaluating novel therapies is challenging in the extremely elderly. Instrumental variable methods identify variables associated with treatment allocation to perform adjusted comparisons that may overcome limitations of more traditional approaches. METHODS AND RESULTS Among all patients aged ≥85 years undergoing percutaneous coronary intervention in nonfederal hospitals in Massachus...

The 'Mendelian randomization' approach uses genotype as an instrumental variable to distinguish between causal and non-causal explanations of biomarker-disease associations. Classical methods for instrumental variable analysis are limited to linear or probit models without latent variables or missing data, rely on asymptotic approximations that are not valid for weak instruments and focus on es...

Economic modeling that yields practical value must cater for effects caused by exogenous variables. AutoRegressive eXogenous approach (ARX) has been widely used in regional economic studies. Instrumental Variable Method is regarded as a preferential method to parametric estimation in ARX modeling. However, traditional instrumental variable methods can only handle single variable which has limit...