What is propensity score matching?
Propensity score matching (PSM) is a quasi-experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics. Using these matches, the researcher can estimate the impact of an intervention.
How do you conduct a propensity score match?
The basic steps to propensity score matching are:
- Collect and prepare the data.
- Estimate the propensity scores.
- Match the participants using the estimated scores.
- Evaluate the covariates for an even spread across groups.
How do you do propensity matching?
What is matching in study design?
Matching is a technique used to avoid confounding in a study design. In a cohort study this is done by ensuring an equal distribution among exposed and unexposed of the variables believed to be confounding. A matched case-control study requires statistical analysis to correct for this phenomenon.
Can a simple propensity score match work?
As discussed previusly, simple propensity score matching cannot account for unobserved characteristics that might explain why a group chooses to enroll in a program and that might also affect outcomes.
How is propensity score matching related to conditional independence?
On the other hand, with propensity score matching, we are appealing to the conditional independence assumption, the idea that matched comparisons imply balance on observed covariates, which ‘recreates’ a situation similar to a randomized experiment where all subjects are essentially the same except for the treatment (Thoemmes and Kim, 2011).
When to use difference in difference in randomization?
DID relies on a less strict exchangeability assumption, i.e., in absence of treatment, the unobserved differences between treatment and control groups arethe same overtime. Hence, Difference-in-difference is a useful technique to use when randomization on the individual level is not possible.
How to calculate propensity score in logistic regression?
In order to run logistic regression above, one must estimate the linear function (2) first. Next obtain propensity score by calculating the probability of the event. If using the Laplace equation, the probability of success is given by: … where s = success or participation observed, and N = number of total observed events.