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Covariate Distribution Balance via Propensity Scores. (arXiv:1810.01370v1 [econ.EM])
来源于:arXiv
The propensity score plays an important role in causal inference with
observational data. Once the propensity score is available, one can use it to
estimate a variety of causal effects in a unified setting. Despite this appeal,
a main practical difficulty arises because the propensity score is usually
unknown, has to be estimated, and extreme propensity score estimates can lead
to distorted inference procedures. To address these limitations, this article
proposes to estimate the propensity score by fully exploiting its covariate
balancing property. We call the resulting estimator the integrated propensity
score (IPS) as it is based on integrated moment conditions. In sharp contrast
with other methods that balance only some specific moments of covariates, the
IPS aims to balance \textit{all} functions of covariates. Further, the IPS
estimator is data-driven, does not rely on tuning parameters such as
bandwidths, admits an asymptotic linear representation, and is
$\sqrt{n}$-consistent an 查看全文>>