In causal inference a matching algorithm assigns a subset of control units to each treated unit. Using combinatorial techniques we explore the support of matching algorithms to provide counting results and investigate the role of the dimension of the covariates’ space
In the first article we present a network based algorithm for probabilistic inference in an undirect...
A tree-based method for identification of a balanced group of observa- tions in casual inference stu...
We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’ ’ (CEM), and ...
In causal inference a matching algorithm assigns a subset of control units to each treated unit. Usi...
We propose a simplified approach to matching for causal inference that simultaneously optimizes bala...
We propose a simplified approach to matching for causal inference that simultaneously optimizes both...
To estimate causal treatment effects, we propose a new matching approach based on the reduced covari...
Matching, especially in its propensity-score flavors, has become an extremely popular evaluation met...
Provides functions to perform matching algorithms for causal inference with clustered data, as descr...
Doctor of PhilosophyDepartment of StatisticsMichael J. HigginsThis dissertation presents an approach...
We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’’ (CEM), and t...
We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal infer...
We discuss a method for improving causal inferences called "Coarsened Exact Matching'' (CEM), and th...
As the counterfactual model of causality has increased in popularity, sociologists have returned to ...
We introduce a new "Monotonic Imbalance Bounding" (MIB) class of matching methods for causal inferen...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
A tree-based method for identification of a balanced group of observa- tions in casual inference stu...
We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’ ’ (CEM), and ...
In causal inference a matching algorithm assigns a subset of control units to each treated unit. Usi...
We propose a simplified approach to matching for causal inference that simultaneously optimizes bala...
We propose a simplified approach to matching for causal inference that simultaneously optimizes both...
To estimate causal treatment effects, we propose a new matching approach based on the reduced covari...
Matching, especially in its propensity-score flavors, has become an extremely popular evaluation met...
Provides functions to perform matching algorithms for causal inference with clustered data, as descr...
Doctor of PhilosophyDepartment of StatisticsMichael J. HigginsThis dissertation presents an approach...
We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’’ (CEM), and t...
We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal infer...
We discuss a method for improving causal inferences called "Coarsened Exact Matching'' (CEM), and th...
As the counterfactual model of causality has increased in popularity, sociologists have returned to ...
We introduce a new "Monotonic Imbalance Bounding" (MIB) class of matching methods for causal inferen...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
A tree-based method for identification of a balanced group of observa- tions in casual inference stu...
We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’ ’ (CEM), and ...
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