Add another robustness check for varying levels of accuracy.

[ml_measurement_error_public.git] / simulations / robustness_check_notes.md

diff --git a/simulations/robustness_check_notes.md b/simulations/robustness_check_notes.md
index e6adc8a5fe7ca9b6a699ab8aeaf9c76aaf37f2c6..0a287a71e3c6d8be79647c53112a4e40a44095eb 100644 (file)
--- a/simulations/robustness_check_notes.md
+++ b/simulations/robustness_check_notes.md
@@ -1,5 +1,14 @@
-# robustness_1.RDS
+# robustness\_1.RDS
 
-Tests how robust the MLE method is when the model for $X$ is less precise. In the main result, we include $Z$ on the right-hand-side of the `truth_formula`. 
+Tests how robust the MLE method for independent variables with differential error is when the model for $X$ is less precise. In the main paper, we include $Z$ on the right-hand-side of the `truth_formula`. 
 In this robustness check, the `truth_formula` is an intercept-only model.
+The stats are in the list named `robustness_1` in the `.RDS` file.
+
+# robustness\_1\_dv.RDS
+
+Like `robustness\_1.RDS` but with a less precise model for $w_pred$.  In the main paper, we included $Z$ in the `outcome_formula`. In this robustness check, we do not.
+
+# robustness_2.RDS
+
+This is just example 1 with varying levels of classifier accuracy.