Quantifying the robustness of inferences: 2 upcoming workshops (August 10 and Feb 18) , one talk (Oct 9)

[Ken Frank]

I hope you are staying safe n' sane.

 

Quantifying the robustness of inferences: 2 upcoming workshops (August 10 and Feb 18) , one talk (Oct 9)

 

August 10th. 11:30a.m.-1:10p.m. sponsored by the American Sociological Association. You will need to register for the conference:

https://www.asanet.org/annual-meeting-2020/registration.

Once you register you will be given access to the zoom link.

 

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.

Oct 9, 2pm-3pm I will be giving a talk at the MSU social psychology colloquium series.  It will likely focus on sensitivity analysis for logistic regressions.

 

Feb 18th. 9a.m.-1p.m. Workshop sponsored by the 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