Last reminder:
I will be giving a workshop on May 19 1pm-5pm (EDT) as part of the American Educational Research Association virtual learning series.
sign up at http://www.aera.net/Professional-Opportunities-Funding/AERA-Virtual-Research-Learning-Series2020
Course Description
Motivation
Statistical inferences are often challenged because of uncontrolled bias. There
may be bias due to uncontrolled confounding variables or non-random selection
into a sample. We will answer the question about what it would take to change
an inference by formalizing the sources of bias and quantifying the discourse
about causal inferences in terms of those sources. For example, we will transform
challenges such as “But the inference of a treatment effect might not be valid
because of pre-existing differences between the treatment groups” to questions
such as “How much bias must there have been due to uncontrolled pre-existing
differences to make the inference invalid?” “QQQ% of the cases would have to be
replaced with cases with no treatment effect to change the inference.”
Approaches
In part I we will use Rubin’s causal model to interpret how much bias there
must be to invalidate an inference in terms of replacing observed cases with
counterfactual cases or cases from an unsampled population. In part II, we will
quantify the robustness of causal inferences in terms of correlations
associated with unobserved variables or in unsampled populations. Calculations
for bivariate and multivariate analysis will be presented using an app: http://konfound-it.com as
well as macros in STATA and R and a spreadsheet for calculating indices
[KonFound-it!].
Ken