A Review of Propensity Score Modelling Approaches

<blockquote> In this article I’ll introduce the concept of a propensity score and what they’re used for before presenting 3 common methodologies. I’ll be discussing the following propensity score models: <ul> <li>Propensity Score Matching with replacement (PSM)</li> <li>Propensity Score Matching without replacement (PSM w/o)</li> <li>Inverse Propensity Score Weighting (IPSW)</li> </ul> <h1>Introduction</h1> The best way to evaluate the impact of a particular intervention or treatment is to run a randomised control trial (RCT). In an RCT you randomly split your population into 2 cohorts and apply the intervention to just one of them – this becomes your treatment group. The cohort that did not receive the intervention is your control. Due to the random assignments between control and treatment there should be no structural differences in characteristics between the two groups. If, after treatment, the treatment group behaves differently (i.e. converts) then we can conclude that this is the result of the intervention. However, there are many scenarios in which it is not possible to run an RCT including but not limited to: <ul> <li>Ethical reasons — e.g. product pricing needs to be kept consistent across users</li> <li>The treatment effect can’t be measured digitally, e.g. an advertising billboard</li> <li>Your tech stack means you can’t create 2 experiences</li> </ul> </blockquote> <a href="https://towardsdatascience.com/a-review-of-propensity-score-modelling-approaches-19af9ecd60d9">Visit Now</a>