Interpretational errors in causal inference and how to avoid them
Abstract:
Pioneering works in causal inference were explicitly grounded in practical disciplines, aiming at formalizing real questions with mathematical definitions. Now, causal inference methods provide an architecture that profoundly regulates what practical questions get asked and how they get answered. Here I consider subtly different approaches for causal inference research, and their implications for theory development and practice. In this process, I formalize an interpretational error that is increasingly apparent in the causal literature, termed "identity slippage". This formalization can be used for error detection whenever policy decisions depend on the accurate interpretation of statistical results, which is nearly always the case. Therefore, broad awareness of identity slippage will aid in the successful translation of data into public good. As an illustration, I present case studies of these errors in mediation analysis and instrumental variable analysis.
My research focuses on the development of innovative methods for statistical causal inference in medicine and epidemiology, which can be applied to large-scale and passively collected longitudinal data, e.g., from electronic health records. A primary objective is to expand the set of policy questions that can be explicitly posed within the language of formal causal and statistical frameworks. I view this as a fundamental step in the development of methods aimed to translate data into human good, and one which occurs at the intersection of statistics and the humanities. Currently, my research applies this approach to answer questions about the mechanisms of healthcare interventions and about optimally triaging scarce treatment resources (like ventilators, vaccines, and highly-trained care providers) in complex health care settings, among other topics. My research also applies a critical lens to the dominant paradigms in statistics and causal inference that structure epidemiologic thought and practice.
