How is the covariate balancing propensity score used?

How is the covariate balancing propensity score used?

We introduce covariate balancing propensity score (CBPS) methodology, which models treatment assignment while optimizing the covariate balance.The CBPS exploits the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment.

Can a person be matched by their propensity score?

Note that only a subset of observed individuals can be paired to form matched samples. The only exposed individuals which can be matched by their propensity score to an unexposed individual are those with propensity scores between 0.3 and 0.8.

When to use propensity score for causal inference?

As is common with many causal inference techniques, an analyst must be cautious when estimating a causal effect using propensity score matching.

Do you have to estimate the propensity score?

There is a catch though – the propensity score must be estimated. And there is no theoretical guidance over how best to do this. Practitioners usually estimate a logit or probit to predict treatment assignation and then check the covariate balance given by the resulting propensity score.

Why are covariates used to predict treatment assignation?

It is meant to predict treatment assignation among the study subjects, i.e. it estimates the likelihood of treatment as a function of observable information. 2. It is meant to balance covariates so that two study subjects with the same propensity score are appreciably similar in observed dimensions.

Is the balancing condition generalizable to higher moments of the covariate distribution?

(It’s important to note that the balancing condition here is generalizable to higher moments of the covariate distribution, not only the first moment. Thus it can accommodate balance in the variances of the covariates as well as the means).