Dear Applied Statistics Workshop Community, Our next meeting will be on October 25 (12:00 EST). Melody Huang presents "Towards Credible Causal Inference under Real-World Complications: Sensitivity Analysis for Generalizability" <When> October 25, 12:00 to 1:30 PM, EST Lunch will be available for pick-up inside CGIS K354. <Where> In-person: CGIS K354 Zoom: https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09 <Abstract> Randomized controlled trials (RCT’s) allow researchers to estimate causal effects in an experimental sample with minimal identifying assumptions. However, to generalize or transport a causal effect from an RCT to a target population, researchers must adjust for a set of treatment effect moderators. In practice, it is impossible to know whether the set of moderators has been properly accounted for. In the following talk, I propose a two parameter sensitivity analysis for generalizing or transporting experimental results using weighted estimators. The contributions in the paper are two-fold. First, I show that the sensitivity parameters are scale-invariant and standardized. Unlike existing sensitivity analyses in external validity, the proposed framework allows researchers to simultaneously account for the bias in their estimates from omitting a moderator, as well as potential changes to their inference. Second, I propose several tools researchers can use to perform sensitivity analysis: (1) graphical and numerical summaries for researchers to assess how robust an estimated effect is to changes in magnitude as well as statistical significance; (2) a formal benchmarking approach for researchers to estimate potential sensitivity parameter values using existing data; and (3) an extreme scenario analysis. While sensitivity tools for routine reporting have been introduced for sensitivity frameworks for outcome modeling approaches, these tools do not yet exist for weighted estimators. Thus, the talk introduces a collection of methods that provide much needed interpretability to sensitivity analyses, and a framework for researchers to transparently and quantitatively argue about the robustness in their estimated effects. <2023 Schedule> GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Calendar: https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm… Best, Jialu -- Jialu Li Department of Government Harvard University jialu_li(a)g.harvard.edu