Toward a Total Causal Error Framework: Extending Cornfield's Insights Across Causal Identification, Representation, and Measurement.

Abstract

Cornfield et al.'s (1959) analysis of smoking and lung cancer showed how explicit assumptions about unobserved confounding and selection mechanisms shape the boundary between statistical association and causal conclusions. Today, causal inference spans diverse fields including economics, statistics, psychology, epidemiology, and evaluation, with distinct traditions and tools. Without a shared framework, it can be difficult to accumulate evidence across studies or to diagnose why results differ when similar questions are studied independently, particularly when differences arise from upstream design and data-generation choices rather than analytic methods alone. While tools for causal identification such as Directed Acyclic Graphs (DAGs) have become widely used across disciplines, they do not naturally foreground threats arising from representation and measurement errors. In contrast, the Total Survey Error (TSE) framework enumerates representation and measurement errors across the survey life cycle and is routinely used to evaluate tradeoffs in survey design and data collection. We propose a Total Causal Error (TCE) framework that integrates causal inference with the TSE perspective to organize risks to causal conclusions along parallel paths of causal identification, representation, and measurement, across the stages of the theoretical estimand, empirical estimand, data collection, data processing, and estimation and interpretation. We propose a version of this framework, provide explicit definitions for its components, illustrate a small number of use cases, and invite further development and coordination across fields.