Analyzing whether workplace smoking bans can reduce the probability of smoking

TL;DR

This study investigates whether indoor workplace smoking bans effectively reduce smoking rates, using observational data and propensity score matching to control for confounders, and finds that bans significantly decrease smoking probability.

Contribution

It applies propensity score matching to observational data to rigorously assess the impact of workplace smoking bans on smoking behavior, addressing bias issues.

Findings

Indoor smoking bans significantly reduce smoking probability.

Propensity score matching effectively controls confounding variables.

Results support implementing workplace smoking restrictions.

Abstract

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.