April 6 finish case replacement framework special topics, change of AERA workshop date to May 19
Ken Frank
Hope you are staying safe n' sane.
Tomorrow, April 6 I will finish the case replacement approach.
Where: https://msu.zoom.us/j/783760435
What: course lectures: "What Would it take to Change your Inference?"
Materials: powerpoint for combined frameworks 3 hours. slides 8-80
What Would it take to Change your Inference?
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?”
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. This generates statements such as “One would have to replace qqq% of the cases with cases with no effect to invalidate the inference.” In part II, we will quantify the robustness of causal inferences in terms of correlations associated with unobserved variables or in unsampled populations. This generates statements such as “An omitted variable would have to be correlated at rrr with the treatment and outcome to invalidate the inference.” 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!].
Format
The format will be a mixture of presentation, individual exploration, and group work. Participants may include graduate students and professors, although all must be comfortable with basic regression and multiple regression. Participants should bring their own laptop, or be willing to work with another student who has a laptop. Participants may choose to bring to the course an example of an inference from a published study or their own work, as well as data analyses they are currently conducting.
All related materials can be found at: https://msu.edu/~kenfrank/research.htm#causal
To participate on zoom you will click on https://msu.zoom.us/j/783760435
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Meeting ID: 783 760 435
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