Upcoming opportunity to learn about our sensitivity analyses

[Ken Frank]

You are invited to drop in on my course lectures on sensitivity analysis. 

Wed April 9 and Monday April 14 from 10:10-11:30 (eastern) on my zoom: https://msu.zoom.us/j/783760435.

Note that this is in the context of a conventional course for graduate students in the social sciences finishing their first year of quantitative method, so focus will be on the students in the course for questions and level of presentation.

Quantifying the robustness of an inference (sensitivity analysis).     

April 9:

          Introduction & Motivation: slides 1-24

         Robustness of Inference to Replacement slides 29-84

  April 14

     Review of April 9, and then Impact Threshold for a Confounding Variable: slides 166-202

Frank, K.A., Lin, Q., Xu, R., Maroulis, S.J., Mueller, A. (2023). Quantifying the Robustness of Causal Inferences: Sensitivity Analysis for Pragmatic Social Science.  Social Science Research. 110, 102815. ERIC:ED628601.

Frank, K.A., Lin, Q., Maroulis, S.J. (accepted). “Causal Inferences from Observational Studies in Education Policy: Towards Pragmatic Social Science” Handbook on Education Policy Research. Published by the American Educational Research Association. Lora Cohen-Vogel, Janelle Scott and Peter Youngs editors.

Related events

Topical workshop ICPSR July 28-Aug 1:

Sensitivity Analysis: Quantifying the Robustness of Inferences to Alternative Factors or Data

Society for Epidemiologic Research, Aug 5, noon-4pm (eastern)

What Would it Take to Change Your Inference? Quantifying the Discourse about Causal Inferences in Epidemiology

1 hour Youtube video from Statistical Horizons

Conference presentations: 

Wednesday, Aug 6, 10:30-12:30 (central) in Nasheville:

2025 Joint Statistical Meetings, Multi-faceted Approaches to Sensitivity Analysis for Observational Studies 

Friday May 16 at 10:15-11:45 in Detroit, Michigan (catch all the action downtown!).

Society for Causal Inference: Multi-faceted Approaches to Sensitivity Analysis for Observational Studies

My paper will be: 

Quantifying Sensitivity to Selection on Unobserved Covariates: Recasting the Coefficient of Proportionality within a Correlational Framework

 

Kenneth A. Frank

Qinyun Lin

Spiro Maroulis

Abstract

Sensitivity analyses can inform evidence-based education policy by quantifying the hypothetical conditions necessary to change an inference. Perhaps the most prevalent index used for sensitivity analyses is Oster’s (2019) Coefficient of Proportionality (COP). Oster’s COP leverages changes in estimated effects and R² when observed covariates are added to a model to quantify how strong selection on unobserved covariates would have to be relative to on observed covariates to nullify an estimated effect.  In this paper, we reconceptualize the COP as a function of unobserved covariates’ correlations with the focal predictor (e.g., treatment) and with the outcome. Our correlation-based approach addresses recent critiques of Oster’s COP while preserving the comparison of selection on unobserved covariates to selection on observed covariates. As importantly, our expressions do not depend on an analyst’s subjective choice of covariates to include in a baseline model, are exact even in finite samples, and can be directly calculated from conventionally reported quantities (e.g., estimated effect, standard error) through the Konfound packages in R or Stata.  Thus, for most published studies in the social sciences our COP index can be easily applied and intuitively interpreted.

For other news, see: https://konfound-it.org/news/

Ken