Parameterizing and Simulating from Causal Models

Understand

Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution.

• This creates many practical complications for statistical inference, even where the problem is non-parametrically identified.
• In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a general way.
• We introduce the `frugal parameterization', which places the causal effect of interest at its centre, and then builds the rest of the model around it.