The Logic of Counterfactuals in Causal Inference

On the Consistency Rule in Causal Inference: An Axiom, Definition, Assumption, or a Theorem?

In two recent communications, Cole and Frangakis and VanderWeele conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need...

Counterfactuals, Causal Inference, and Historical Analysis

Causal Inference

This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (NeymanRubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper...

Causal inference based on counterfactuals

BACKGROUND The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. DISCUSSION This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, ...

On the consistency rule in causal inference: axiom, definition, assumption, or theorem?

In 2 recent communications, Cole and Frangakis (Epidemiology. 2009;20:3-5) and VanderWeele (Epidemiology. 2009;20:880-883) conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency ...