Causal Inference with Counterfactuals

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, ...

Causal inference in statistics: An overview

This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of al...

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...

Stochastic counterfactuals and stochastic sufficient causes.

Most work in causal inference concerns deterministic counterfactuals; the literature on stochastic counterfactuals is small. In the stochastic counterfactual setting, the outcome for each individual under each possible set of exposures follows a probability distribution so that for any given exposure combination, outcomes vary not only between individuals but also probabilistically for each par...