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 FormulaMethods in BayesiaLab
pageCausal Effect Estimation in BayesiaLab with Graph SurgerypageCausal Effect Estimation in BayesiaLab with Likelihood MatchingLast updated