  The rapid increase of smoking-related diseases and deaths globally is driving
us to find an effective approach to reduce the smoking rate. This study aims to
determine whether indoor smoking bans at workplaces can effectively reduce the
smoking rate. The Smokeban dataset used for this study is an observational
dataset that contains some socio-demographic factors, whether people smoke, and
whether smoking bans exist. Since the observational data used in the study did
not randomize people into with-smoking-bans group and without-smoking-bans
group, confounders may cause bias in the estimation of whether the smoking bans
can reduce smoking rates. The propensity score matching(PSM) method can reduce
these biases via using a logistic regression model to predict the similarities
of people in those 2 groups and using the nearest neighbour matching technique
to match people who are the most similar. After reducing the bias, another
regression model was created to interpret the relationship between the
probability of smoking and the indoor smoking bans. We conclude by arguing that
with the existence of indoor smoking bans, the probability of people who smoke
can be decreased greatly.
