Causal inference based on counterfactuals.

M Höfler

doi:10.1186/1471-2288-5-28

Abstract

BackgroundThe counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.DiscussionThis 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, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures.SummaryCounterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

Highlights

The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies
Summary: Counterfactuals are the basis of causal inference in medicine and epidemiology
The estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept

Summary

Introduction

The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. The only sine qua non condition for a causal effect in an individual is the precedence of the factor to its effect, and 100% evidence for causality is impossible. This insight dates back at least to the 18th century Scottish philosopher David Hume [[1]; 2 chap. The question is how much evidence for a causal effect one can collect in practice and what statistical models can contribute to such evidence. A variety of conceptual as well as practical issues in estimating counterfactual causal effects are discussed

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Discussion

Conclusion