TECHNICAL REPORT R-51 First version: Dec, 2019 Last revision: March, 2021 Causal Inference and Data Fusion in Econometrics∗ Paul Hunermund¨ Elias Bareinboim Copenhagen Business School Columbia University
[email protected] [email protected] Learning about cause and effect is arguably the main goal in applied economet- rics. 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 investiga- tion. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, selection-biased sam- ples, causal effects need to be learned from surrogate experiments with imperfect compliance, and causal knowledge has to be extrapolated across structurally hetero- geneous populations. A powerful 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 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 learn- ing that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances made in this literature that have the potential to contribute to econo- metric methodology along three broad dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the above-mentioned problems can be addressed in full generality.