NIeCer 201: Causal Inference from Observational Studies [CAUSIT]

​COURSE DIRECTOR 

​Dr. Tarun Bhatnagar, MD, PhD, PGDBE


ABOUT THE COURSE: 

Causal inference is a core task of science, regardless of whether the study is randomized or nonrandomized. Causal inference is a complex scientific task that relies on triangulating evidence from multiple sources and on the application of a variety of methodological approaches. The scientific literature is plagued by studies in which the causal question is not explicitly stated and the investigators’ unverifiable assumptions are not declared. Using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results. Eliminating the causal–associational ambiguity has practical implications for the quality of observational research. There is a need for a course that would help researchers in health and related domains to generate and analyze data to make causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis. This course will focus on the identification and estimation of causal effects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under different interventions. Therefore, in order to improve the current situation, it is important that the health researchers in the country are oriented fully to the principles and practice of epidemiologic methods for making causal inferences from observational studies. The relevance of this course only increases in the current situation because of the COVID-19 pandemic where researchers are trying to identify the effects of multiple medical interventions and public health strategies towards its medical management, control and prevention.

The objectives of this course will be to understand the design of observational epidemiological studies, comprehend the principles of causality, and to know the epidemiological and analytical methods to make causal inference from observational studies.