I hope you are staying safe n' sane.
We have 2 upcoming workshops on "what would it take to change your inference."
August 10th. 11:30a.m.-1:30p.m. sponsored by the American Sociological Association. You will need to register for the conference:
https://www.asanet.org/annual-meeting-2020/registration, specific details about how to register for the workshop should be available by the week of July 20.
3115 - What would it take to Change your Inference? Quantifying the Discourse about Causal Inferences in the Sociology
Mon, August 10, 8:30 to 10:10am PDT (11:30am to 1:10pm EDT),
Description
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 turn concerns about
potential bias into questions about how much bias there must be to invalidate
an inference. For example, challenges such as “But the inference of a treatment
effect might not be valid because of pre-existing differences between the
treatment groups” are transformed to questions such as “How much bias must there
have been due to uncontrolled pre-existing differences to make the inference
invalid?”
By reframing challenges about bias in terms of specific quantities, this course
will contribute to scientific discourse about uncertainty of causal inferences.
Critically, while there are other approaches to quantifying the sensitivity of
inferences(e.g., Robins and Rotnitzky, 1995; Rosenbaum and Rubin 1983,
Rosenbaum, 2000), the approaches presented in this workshop based on
correlations of omitted variables (Frank, 2000) and the replacement of cases
(Frank and Min, 2007; Frank et al, 2013) have greater intuitive appeal. In this
sense the techniques align well issues of power and inequality because they
allow a diverse set of voices to participate in conversations about causal
inferences.
In part I, we 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 (e.g., Frank et al,
2013). In part II, we quantify the robustness of causal inferences in terms of
correlations associated with unobserved variables or in unsampled populations
(e.g., Frank 2000). Calculations will be presented using the app
http://konfound-it.com with links to STATA and R modules. The format will be a
mixture of presentation, individual exploration, and group work.
Feb 18th. 9a.m.-1p.m. eastern. American Statistical Association Conference on Statistical Practice (CSP2021). Conference registration is at: https://ww2.amstat.org/meetings/csp/2021/registration.cfm. Details about how to register for the workshop to follow.
Check out the R Shiny app for sensitivity analysis
http://konfound-it.com or the Blog https://www.konfound-it.org/
Check out our Causal inference and COVID working paper:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3607967
Ken