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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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