Causal Inference

Graphical Tools for Linear Structural Equation Modeling

July, 2015

Abstract

This paper surveys graphical tools developed in the past three decades that are applicable to linear structural equation models (SEMs). These tools permit researchers to answer key research questions by simple path-tracing rules, even for highly complex models. They include parameter identification, causal effect identification, regressor selection, selecting instrumental variables, finding testable implications of a given model, identifying equivalent models and estimating counterfactual relationships.

A Linear “Microscope” for Interventions and Counterfactuals

March, 2017

Abstract

This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams. By producing closed form solutions, linear analysis allows for swift assessment of how various features of the model impact the questions under investigation. We discuss conditions for identifying total and direct effects, representation and identification of counterfactual expressions, robustness to model misspecification, and generalization across populations.

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