Many thanks for your presentation. I have been reading on causal inference and judea pearls do why.
One thing i do not understand is the relationship (if any) between the estimand computed in step 2 and the propensity score matching. Are these two processes linked?
(Also when we say estimand is this what we mean by SCM (structural causal model) .
e.g. i thought that the estimand is what is used to compute the causal effect, and this estimand in turn is computed from the observed data using conditioning. However when we get to step 3 and start calculating 'propensity scores' to get a match between no treatment and treatment where does the estimand come into this? Is the propensity score matching an approximation to the estimand? How is the propensity score calculated in dowhy package... some source code may help me understand better.
Many thanks for your presentation. I have been reading on causal inference and judea pearls do why.
One thing i do not understand is the relationship (if any) between the estimand computed in step 2 and the propensity score matching. Are these two processes linked?
(Also when we say estimand is this what we mean by SCM (structural causal model) .
e.g. i thought that the estimand is what is used to compute the causal effect, and this estimand in turn is computed from the observed data using conditioning. However when we get to step 3 and start calculating 'propensity scores' to get a match between no treatment and treatment where does the estimand come into this? Is the propensity score matching an approximation to the estimand? How is the propensity score calculated in dowhy package... some source code may help me understand better.