Our first Statistics Journal Club of the semester is next Friday, February 2^{nd}, in Votey 209 from noon to 1:00.

We will discuss **sections 1 and 2** of the paper *Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study *(attached).

There are some technical passages in Section 2, but the paper provides a clear introduction and review of propensity scores, which are widely used to estimate causal treatment effects from observational studies.

Pizza will be served.

SUMMARY

"Estimation of treatment eects with causal interpretation from observational data is complicated because

exposure to treatment may be confounded with subject characteristics. The propensity score, the

probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting

for confounding: methods based on stratication of observations by quantiles of estimated propensity

scores and methods based on weighting observations by the inverse of estimated propensity scores. We

review popular versions of these approaches and related methods oering improved precision, describe

theoretical properties and highlight their implications for practice, and present extensive comparisons of

performance that provide guidance for practical use. Copyright ? 2004 John Wiley & Sons, Ltd."