Learn

Congratulations! You have learned a lot about propensity score methods in this lesson and are quickly becoming a master of causation (not just correlation).

In this lesson, you learned:

- There are five stages of using propensity scores in causal inference.
- A propensity score is computed by predicting the probability of treatment from the other observed variables.
- We use propensity scores in Inverse Probability of Treatment Weighting (IPTW) to create balance across observed variables.
- We can check balance between treatment and control groups across variables using the cobalt package functions
`bal.tab()`

,`bal.plot()`

, and`love.plot()`

. - A poor propensity score model may lead to biased estimates of the treatment effect, so it is very important that we find the best model possible.
- We get an estimate of the treatment effect by creating a regression model with IPTW weights.

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