Effect Estimation

Estimation

Returning to the original version of the CDAG, without the hidden variable, we are now ready to proceed with the estimation. However, this CDAG is only a qualitative representation of our theory about the DGP. We now need to consider this graph as a model representing the joint probability distribution of our three variables P(X, Y, Z).

We do not yet need to determine what this probability function is; we simply need to consider this graph as a non-parametric probability function linking X, Y, and Z. This will help us understand what it means to adjust for Z to estimate the causal effect.

Estimation Methods

General Methods

pageGraph SurgerypageAdjustment Formula

Methods in BayesiaLab

pageCausal Effect Estimation in BayesiaLab with Graph SurgerypageCausal Effect Estimation in BayesiaLab with Likelihood Matching