Forecasting Causal Effects of Interventions versus Predicting Future Outcomes

Christian Gische, Stephen G. West, Manuel C. Voelkle

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The present article provides a didactic presentation and extension of selected features of Pearl’s DAG-based approach to causal inference for researchers familiar with structural equation modeling. We illustrate key concepts using a cross-lagged panel design. We distinguish between (a) forecasts of the value of an outcome variable after an intervention and (b) predictions of future values of an outcome variable. We consider the mean level and variance of the outcome variable as well as the probability that the outcome will fall within an acceptable range. We extend this basic approach to include additive random effects, allowing us to distinguish between average effects of interventions and person-specific effects of interventions. We derive optimal person-specific treatment levels and show that optimal treatment levels may differ across individuals. We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels.

Original languageEnglish (US)
Pages (from-to)475-492
Number of pages18
JournalStructural Equation Modeling
Volume28
Issue number3
DOIs
StatePublished - 2021

Keywords

  • Causal inference
  • DAG
  • cross-lagged panel
  • structural equation modeling

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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