Exploring Graphical Causal Models
Graphical models epitomize the lexicon of causality as a tool for elucidating one’s conceptual understanding of causal relationships.
Consider, for instance, the principle of conditional independence regarding potential outcomes; a concept that enables the isolation of a treatment’s effect on an outcome, distinct from the influence of extraneous variables. An illustrative case is the administration of medication to patients: if only the most severely ill receive the treatment, it might erroneously appear that the medication exacerbates health conditions. This misconception arises from the conflation of illness severity with the medication’s impact. Stratifying the patient population into categories based on severity and subsequently examining the medication’s effect within these subgroups yields a more accurate assessment of its true impact. This process, termed “controlling for” or “conditioning...
