A tailored use of the mahalanobis distance matching for causal effects estimation: A simulation study

Abstract

Selection bias is a fundamental reason why the estimation of treatment effects in observational studies is not as straightforward as in well-designed experiments. We introduce an improved and generalized matching method based on redefining how the Mahalanobis distance can be used for matching to reduce covariate imbalance and improve the efficiency of treatment effect estimates. The motivation for this new proposal is based on the inability of the previously proposed covariate balancing rank-based Mahalanobis distance (CBRMD) method in explicitly creating fair play in its weight allocation. Our newly proposed approach has a particularly interesting parameter that provides insight into the behaviour of less variable and less aberrant weights that can maximize covariate balance. We illustrated the proposed technique by analyzing simulated data and two real-life datasets. In addition, a web-based Shiny application written in R statistical language was developed and deployed online to implement the proposed technique. Our new proposal is a promising alternative tool for reducing selection bias in the estimation of causal effects.

Keywords

• Causal inference
• Observational study
• Matching
• Covariate balance
• Propensity score
• Monte Carlo simulation