Paul Hünermund, Elias Bareinboim
arXiv 19 Dec 2019 · Econometrics · publishedEconometrics Journal (2023) · 68 citations (OpenAlex)
arXiv:1912.09104 · PDF · DOI · OpenAlex · Extracted main text
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. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, 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 causal inference framework is required to tackle these challenges, which plague most data analysis to varying degrees. Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer an effective middle ground between these two literature streams.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pearl, J (2009) Causality: Models, Reasoning, and Inference\/ (2nd ed.) | 1.000 | 12 | 4 | 100% |
| 2 | Pearl, J (1995) Causal diagrams for empirical research | 1.000 | 9 | 3 | 100% |
| 3 | Bareinboim, E. and J. Pearl (2016) Causal inference and the data-fusion problem self | 1.000 | 5 | 3 | 100% |
| 4 | Haavelmo, T (1943) The statistical implications of a system of simultaneous equations | 0.928 | 4 | 3 | 100% |
| 5 | Heckman, J. J. and E. J. Vytlacil (2007) Econometric evaluation of social programs, part 1: Causal models, structural models and econometric policy evaluation | 0.928 | 4 | 3 | 100% |
| 6 | Lee, S., J. D. Correa, and E. Bareinboim (2019) General identifiability with arbitrary surrogate experiments | 0.874 | 5 | 2 | 100% |
| 7 | Strotz, R. H. and H. O. A. Wold (1960) Recursive vs.\ nonrecursive systems: An attempt at synthesis (part i of a triptych on causal chain systems) | 0.874 | 5 | 2 | 100% |
| 8 | Woodward, J (2003) Making Things Happen | 0.811 | 4 | 2 | 100% |
| 9 | Duflo, E., R. Glennerster, and M. Kremer (2008) Using randomization in development economics research: A toolkit | 0.737 | 3 | 2 | 100% |
| 10 | Pearl, J (1988) Probabilistic Reasoning in Intelligent Systems | 0.737 | 3 | 2 | 100% |
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