An alternative method in estimating propensity scores with conditional inference tree in multilevel data: A case study

A multilevel structure of data is widely used in a variety of social science settings. To investigate the effects of interventions, researchers often conduct observational studies that use large scale secondary data and incorporate propensity score methods; this is beneficial in performing causal inference in non-randomized observational studies. The standard propensity score uses a logistic regression approach; however, this approach could be outperformed by alternative methods based on statistical learning and data mining algorithms. To date, little research had addressed data utilizing mining methods within propensity score design, especially with multilevel observational data. The purpose of this study is to examine the performance of propensity scores associated with the use of stratification, estimated by a multilevel logistic versus a conditional inference tree using large scale secondary data derived from the Programme for International Student Assessment. The results showed that a conditional inference tree more conservatively estimates the treatment effect. In addition, the covariate balance result showed that the CIT better produced a randomized treatment / control design than did the multilevel logistic regression.