Applying Causal Inference Methods to Improve Identification of Health and Healthcare Disparities

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Applying Causal Inference Methods to Improve Identification of Health and Healthcare Disparities

Counterfactual analysis is a dominant causal paradigm in recent literature in statistics. This type of analysis provides a framework for better understanding racial/ethnic disparities in health and healthcare, and for rigorously conceptualizing scientific questions that are relevant in the field of health disparities. In typical gold standard randomized control trial (RCT), the “treatment” is randomized to a set of respondents. The causal effect can then be identified as a difference in outcomes between the treatment and control groups. The underlying assumption is that the randomization allows for the approximation of the difference between the outcome if an individual received the treatment and the outcome if the same individual did not receive the treatment. The reason that this is a counterfactual is that an individual cannot simultaneously be in both the treatment and control group. Race/ethnicity is not “manipulable” in this way and cannot be randomized. However, the counterfactual causal inference framework can be readily applied to quantify the effect of interventions aimed at eliminating racial disparities in health.

In the first part of the webinar, Dr. Cook will discuss methods that are concordant with the IOM definition of racial/ethnic healthcare disparities which defines disparities as all differences except those due to clinical appropriateness and need and patient preferences. In the second part of the webinar, Dr. Valeri will discuss the application of the counterfactual framework to investigating determinants of health disparities, with a particular focus on black-white disparities in cancer survival and the role of stage at diagnosis.

PLAY RECORDING (1h 20min)

For the PowerPoint slides from the webinar, click here.

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