Identification with DAGs: Introduction with simple simulations
En español
In this post I want to share with you some introductory ideas on how Directed Acyclical Graphs (DAGs) are used for causal identification. I am also sharing a few (Stata based) numerical simulations (here), that can be illustrative of their use in a regression application.
The DAG approach has been around for at least a decade now, and is described in extent in the excellent book by Pearl and Mackenzie (2018)’s “The Book of Why”. There’s so much going on in the book that I will be writing more about it in a future post.
Using loops to run (and export) many regressions / Usando loops para correr (y exportar) múltiples regresiones
español The use of loops becomes essential when needing to perform repetitive calculations. Looping has many advantages, for example, when needing to do corrections in all the calculations specifications. So here are some interesting features that you would like to do when implementing a loop to run many regressions, and export their outputs: Choose the appropriate method for the regression according to the type of dependent variable. For instance, you might want to estimate the model using OLS (regress) when the dependent variable is continuous and or a probit or a logit model when it is discrete (a dummy variable). Progressively add explanatory variables to the model and export all the output in a single table. This can be done using outreg2 ‘s replace and append options, but if you want instead to write a single command line inside a loop you will have to make the appropriate changes. So assume that you want to estimate a number of econometric models that are quite similar in terms of the explanatory variables that are incorporated, but differ between them in terms of the dependent variables, for example : Model 1: outcome1=b1*x1+b2*x2+b3*X3+b4*X4+e Model 2: outcome2=b1*x1+b2*x2+b3*X3+b4*X4+e In addition you also want to progressively add sets of explanatory variables. So for instance you…