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Online Causal Inference Seminar

Event Details:

Tuesday, November 22, 2022
8:30am - 9:30am PST

This event is open to:

General Public

Free and open to the public

Tuesday, November 22, 2022 [Link to join]

(ID: 996 2837 2037, Password: 386638)

  • Speaker: Julie Josse (Inria)
  • Title: Causal inference for brain trauma: leveraging incomplete observational data and RCT
  • Discussant: Elizabeth Stuart (Johns Hopkins University)
  • Abstract: The simultaneous availability of observational and experimental data for the same medical question about the effect of a treatment is an opportunity to combine their strengths and address their weaknesses. In this presentation, I will illustrate the methodological challenges we faced in answering a medical question about the effect of tranexamic acid administration on mortality in patients with traumatic brain injury in the context of critical care management. First, we had access to a large French observational registry on severely traumatized patients, but almost all variables were incomplete. We considered different sets of hypotheses under which causal inference is possible despite the missing attributes and discussed corresponding approaches to estimating the average treatment effect, including generalized propensity score methods and multiple imputation. Second, results from an international RCT were published that did not necessarily agree with those obtained from the observational study. This led us to investigate generalization problems where the trial data are considered a biased sample of a target population and we want to predict the treatment effect on the target population represented by the observational data. We focus on the Inverse Propensity of Sampling Weighting (IPSW) estimator and establish finite-sample and asymptotic results on different versions of this estimator. In addition, we have studied how including covariates that are unnecessary for identifiability can have an impact on the asymptotic variance. Finally, I will quickly mention solutions for dealing with sporadic missing values in both data sources in this generalization framework and systematic missing values when a variable is not available in one or both data sources. [Paper #1, #2, #3]

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