Add to ORKGWe introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible unweighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not always require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile comprises the characteristics of a single individual. Profile matching achieves c...
We propose a simplified approach to matching for causal inference that simultaneously optimizes both...
Along with increasing amounts of big data sources and increasing computer performance, real-world ev...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
Introduction: Matching could be defined as “any method that aims to equate (or “balance”) the distri...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
To estimate causal treatment effects, we propose a new matching approach based on the reduced covari...
In casual-effect relationship research, similarity of groups being compared in terms of covariates o...
Matching, especially in its propensity-score flavors, has become an extremely popular evaluation met...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
In observational studies, matching can be used to remove bias between treated and control subjects. ...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
In observational studies, matching can be used to remove bias between treated and control subjects. ...
Matching on covariates is a well-established framework for estimating causal effects in observationa...
This thesis consists of three papers on matching and weighting methods for causal inference. The fir...
In the context of a binary treatment, matching is a well-established approach in causal inference. H...
We propose a simplified approach to matching for causal inference that simultaneously optimizes both...
Along with increasing amounts of big data sources and increasing computer performance, real-world ev...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
Introduction: Matching could be defined as “any method that aims to equate (or “balance”) the distri...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
To estimate causal treatment effects, we propose a new matching approach based on the reduced covari...
In casual-effect relationship research, similarity of groups being compared in terms of covariates o...
Matching, especially in its propensity-score flavors, has become an extremely popular evaluation met...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
In observational studies, matching can be used to remove bias between treated and control subjects. ...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
In observational studies, matching can be used to remove bias between treated and control subjects. ...
Matching on covariates is a well-established framework for estimating causal effects in observationa...
This thesis consists of three papers on matching and weighting methods for causal inference. The fir...
In the context of a binary treatment, matching is a well-established approach in causal inference. H...
We propose a simplified approach to matching for causal inference that simultaneously optimizes both...
Along with increasing amounts of big data sources and increasing computer performance, real-world ev...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...