Entropy matching and propensity score matching are two statistical methods used to reduce confounding in observational studies where the goal is to estimate the causal effect of a treatment or intervention. These methods have their individual approaches and are appropriate for different situations. Here's an explanation of both methods:
Propensity Score Matching (PSM)
Propensity score matching involves pairing units (such as individuals or schools) that have similar values of a propensity score, which is the probability of being assigned to the treatment group given a set of observed covariates.
Key Features:
The primary objective of PSM is to mimic a randomized control trial by balancing the distribution of observed covariates between the treatment and control groups. It typically involves computing the propensity score using logistic regression, and then matching treatment and control units based on these scores. Nearest neighbor, caliper matching, and stratification are popular methods to implement PSM.
Advantages:
PSM is relatively easy to understand and implement. It is widely used and accepted, with extensive literature and empirical applications supporting its use.
Disadvantages:
PSM only balances observed covariates, leaving potential for bias due to unobserved variables. The quality of matching heavily depends on the choice of covariates and the form of the propensity score model.
Entropy Balancing (EB)
Entropy balancing is a reweighting method that assigns weights to the units in the treatment and control groups to directly balance the moments (for example, means, variances) of the covariate distributions across groups. It uses a pre-specified target such as mean or variance and finds weights that minimize the entropy measure subject to these balance constraints.
Key Features:
EB assigns weights to each observation instead of creating matches, in a way that the weighted covariate distributions of the treatment and control groups are directly equivalent according to specified balance targets. The method solves a convex optimization problem to find the weights, typically requiring less manual tuning compared to PSM. Entropy balancing can ensure exact balance on specified covariate moments, enhancing the credibility of causal inferences.
Advantages:
EB does not require explicit pairing or matching of units, simplifying the process in large samples. It can achieve excellent balance on covariates, potentially leading to better control of confounding. The method efficiently handles continuous and categorical variables.
Disadvantages:
EB is less intuitive and harder to explain to non-experts compared to PSM. It may not be as familiar or widely used as PSM, potentially affecting peer acceptance in some fields.
Choosing Between the Two
The choice between entropy balancing and propensity score matching often depends on the specifics of the data set and the study goals. If exact balance on covariate distributions is crucial and the study has a large sample size, entropy balancing might be preferable. On the other hand, if the research context values methods that are well-understood and commonly accepted, or if detailed matching is important, propensity score matching may be more suitable. Both methods aim to facilitate more accurate causal inference by adjusting for differences in baseline covariates between treated and control groups in observational studies.
