Add to ORKGConventionally, the construction of a pair-matched sample selects treated and control units and pairs them in a single step with a view to balancing observed covariates x and reducing the heterogeneity or dispersion of treated-minus-control response differences, Y. In contrast, the method of cardinality matching developed here first selects the maximum number of units subject to covariate balance constraints and, with a balanced sample for x in hand, then separately pairs the units to minimize heterogeneity in Y. Reduced heterogeneity of pair differences in responses Y is known to reduce sensitivity to unmeasured biases, so one might hope that cardinality matching would succeed at both tasks, balancing x, stabilizing Y. We use cardinality m...
We propose a simplified approach to matching for causal inference that simultaneously optimizes bala...
A basic feature of many field experiments is that investigators are only able to randomize clusters ...
In an effort to detect hidden biases due to failure to control for an unobserved covari-ate, some ob...
Conventionally, the construction of a pair-matched sample selects treated and control units and pair...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
In observational studies of treatment effects, matched samples are created so treated and control gr...
In some observational studies of treatment effects, matched samples are created so treated and contr...
This thesis unites three papers discussing new strategies for matched pair designs using observation...
Comparative effectiveness studies can identify the causal effect of treatment if treatment is unconf...
This thesis unites three papers discussing new strategies for matched pair designs using observation...
This thesis consists of three papers on matching and weighting methods for causal inference. The fir...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
Causal inference with observational data has drawn attention across various fields. These observatio...
A distinctive feature of a clustered observational study is its multilevel or nested data structure ...
We propose a simplified approach to matching for causal inference that simultaneously optimizes bala...
A basic feature of many field experiments is that investigators are only able to randomize clusters ...
In an effort to detect hidden biases due to failure to control for an unobserved covari-ate, some ob...
Conventionally, the construction of a pair-matched sample selects treated and control units and pair...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
In observational studies of treatment effects, matched samples are created so treated and control gr...
In some observational studies of treatment effects, matched samples are created so treated and contr...
This thesis unites three papers discussing new strategies for matched pair designs using observation...
Comparative effectiveness studies can identify the causal effect of treatment if treatment is unconf...
This thesis unites three papers discussing new strategies for matched pair designs using observation...
This thesis consists of three papers on matching and weighting methods for causal inference. The fir...
Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferenc...
In observational studies, treated subjects and controls are often matched to remove bias in pre-trea...
Causal inference with observational data has drawn attention across various fields. These observatio...
A distinctive feature of a clustered observational study is its multilevel or nested data structure ...
We propose a simplified approach to matching for causal inference that simultaneously optimizes bala...
A basic feature of many field experiments is that investigators are only able to randomize clusters ...
In an effort to detect hidden biases due to failure to control for an unobserved covari-ate, some ob...