What propensity score tells us?

What propensity score tells us?

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial.

What is the purpose of propensity score matching?

In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment.

What is a high propensity score?

A High-Dimensional Propensity Score (HDPS) is a probability score between zero and one typically obtained from a logistic regression model that reflects the likelihood of the outcome regressed, be it the likelihood of belonging to a group or having a disease.

Is propensity score matching good?

Abstract. Propensity score matching (PSM) has been widely used to reduce confounding biases in observational studies. Its properties for statistical inference have also been investigated and well documented.

Why use propensity score matching instead of regression?

The estimates of the propensity score are more precise (the standard errors are much smaller) than the estimates from logistic regression. As the number of events per confounder increases, the precision of the logistic regression increases. OR, odds ratio.

How do you use propensity scores?

The basic steps to propensity score matching are:

1. Collect and prepare the data.
2. Estimate the propensity scores.
3. Match the participants using the estimated scores.
4. Evaluate the covariates for an even spread across groups.

What is wrong with propensity score matching?

In 2016, Gary King and Richard Nielsen posted a working paper entitled Why Propensity Scores Should Not be Used for Matching, and the paper was published in 2019. They showed that the matching method often accomplishes the opposite of its intended goal—increasing imbalance, inefficiency, model dependence, and bias.

Why you shouldn’t use propensity score matching?

Abstract: We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal — thus increasing imbalance, inefficiency, model dependence, and bias.