Review of Causal Inference in Forensic Medicine
DSA ADS Course - 2021
Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation
Causality - intuitive causation vs. probabilistic causation. Probabilistic causation is optimal yet discuss other techniques to seek true causation. Sometimes true causation is impossible to find and discuss optimal strategy under such circumstances.
The primary aim of forensic medical analysis is to provide legal factfinders with evidence regarding the causal relationship between an alleged action and a harmful outcome. Despite existing guides and manuals, the approach to formulating opinions on medicolegal causal inference used by forensic medical practitioners, and how the strength of the opinion is quantified, is mostly lacking in an evidence-based or systematically reproducible framework. In the present review, we discuss the literature describing existing methods of causal inference in forensic medicine, especially in relation to the formulation of expert opinions in legal proceedings, and their strengths and limitations. Causal inference in forensic medicine is unique and different from the process of establishing a diagnosis in clinical medicine. Because of a lack of tangibility inherent in causal analysis, even the term “cause” can have inconsistent meaning when used by different practitioners examining the same evidence. Currently, there exists no universally applied systematic methodology for formulating and assessing causality in forensic medical expert opinions. Existing approaches to causation in forensic medicine generally fall into two categories: intuitive and probabilistic. The propriety of each approach depends on the individual facts of an investigated injury, disease, or death. We opine that in most forensic medical settings, probabilistic causation is the most suitable for use and readily applicable. Forensic medical practitioners need, however, be aware of the appropriate approach to causation for different types of cases with varying degrees of complexity.