In research, it is crucial to accurately estimate treatment effects and analyze experimental results. Common methods include comparing outcome differences between different groups and using linear regression models for analysis. However, observational studies may have significantly different distributions of confounding variables between control and treatment groups, leading to errors in estimating treatment effects.
The propensity score methods can address this issue by weighting or matching samples to approximate the scenario of a randomized experiment and allow for more accurate estimation of treatment this paper.
We use propensity score methods to analyze three datasets from observational studies and draw conclusions different from those in the original text. Furthermore, we simulate three scenarios, and the results demonstrate the superiority of propensity score methods over methods such as linear regression in addressing selection bias.
Therefore, it is essential to thoroughly consider the characteristics of the data and select appropriate methods to ensure reliable conclusions in practical data analysis.