Causal Inference and Data Fusion in Econometrics

Paul Hünermund*, Elias Bareinboim

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected, and the substantive knowledge that is available about the phenomenon under investigation. For instance, unobserved confounding factors threaten the internal validity of estimates; data availability is often limited to nonrandom, selection-biased samples; causal effects need to be learned from surrogate experiments with imperfect compliance; and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful and flexible causal inference framework is required in order to tackle all of these challenges, which plague essentially any data analysis to varying degrees. Building on the structural perspective on causality introduced by Haavelmo (1943) and the graph-theoretic approach proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal inference that allow us to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we review recent advances made in this literature that have the potential to contribute to econometric methodology along three broad dimensions. First, they provide a unified and comprehensive framework for causal learning, in which the above-mentioned problems can be addressed in generality. Second, due to their origin in AI, they come together with sound, efficient, and complete (to be formally defined) algorithmic criteria for automation of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the analytical rigor of structural econometrics with the flexibility of the potential outcomes framework, and thus offer a valuable complement to these two literature streams.
Original languageEnglish
Article numberutad008
JournalEconometrics Journal
Number of pages42
Publication statusPublished - 10 Mar 2023

Bibliographical note

Epub ahead of print. Published online: 10 March 2023


  • Causal inference
  • Directed acyclic graphs
  • Causal diagrams
  • Artificial intelligence
  • Data fusion

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