Upcoming Talks
Multi-faceted Approaches to Sensitivity Analysis for Observational Studies
May 16, 2025 (10:15am–11:45am Eastern time in Detroit, MI).
Quantifying Sensitivity to Selection on Unobserved Covariates: Recasting the Coefficient of Proportionality within a Correlational Framework
2025 Joint Statistical Meetings, Multi-faceted Approaches to Sensitivity Analysis for Observational Studies
August 6, 2025 (10:30am–12:30pm Central time in Nashville, TN).
Presenters: 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.
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Past Talks
Quantifying Sensitivity to Selection on Unobservables: Refining Oster’s Coefficient of Proportionality
August 4, 2024 (8:30pm–9:30pm Pacific time).
Robustness of Inference to Replacement & Fragility for Logistic Regression and Hazard Functions
University of Gothenburg, Sweden. School of Public Health
May 23, 2024 (1:00pm–2:45pm Sweden time).
Robustness of Inference to Replacement & Fragility for Logistic Regression and Hazard Functions
Michigan State University Biostats
April 11, 2024 (3:30pm–4:30pm Eastern time, E111 Fee hall)
Quantifying the Robustness of Causal Inferences: Sensitivity Analysis for Pragmatic Social Science
Emory University: Workshop on Advanced Research Methods
March 21, 2024 (8:30pm–9:30pm Eastern time, Virtual)
Quantifying Sensitivity to Selection on Unobservables: Refining Oster’s Coefficient of Proportionality
Association for Education Finance and Policy
March 15, 2024 (8:00am–9:45am Eastern time, Baltimore Marriott Waterfront, Room 12 — Falkland)
Communicating the Robustness of COVID-19 Studies
AERA Virtual Research Learning Series
May 1, 2020