Empirical scientists seek not just surface descriptions of the observed data, but deeper explanations of why things happened the way they did, and how the world would be like had things happened differently. With the unprecedented accumulation of data (or, “big data”), researchers are becoming increasingly aware of the fact that traditional statistical techniques, including those based on artificial intelligence and machine learning, must be enriched with two additional ingredients in order to construct such explanations:
- the ability to integrate data from multiple, heterogeneous sources, and
- the ability to distinguish causal from associational relationships.
In this talk, I will present a theory of causal generalization that provides a principled way for fusing pieces of empirical evidence coming from multiple, heterogeneous sources. I will first introduce a formal language capable of encoding the assumptions necessary to express each problem instance. I will then present conditions and algorithms for deciding whether a given problem instance admits a consistent estimate for the target effects and, if feasible, fuse information from various sources to synthesize such an estimate. These results subsume the analyses conducted in various fields in the empirical sciences, including “external validity,” “meta-analysis,” “heterogeneity,” “quasi-experiments,” “transportability,” and “sampling selection bias.” I will conclude by presenting new challenges and opportunities opened by this research.
Speaker Biography
Elias Bareinboim is a postdoctoral scholar (and was a Ph.D. student) in the Computer Science Department at the University of California, Los Angeles, working with Judea Pearl. His interests are in causal and counterfactual inferences and their applications. He is also broadly interested in artificial intelligence, machine learning, robotics, and philosophy of science. His doctoral thesis provides the first general framework for solving the generalizability problems in causal inference – which has applications across all the empirical sciences. Bareinboim’s recognitions include the Dan David Prize Scholarship, the Yahoo! Key Scientific Challenges Award, the Outstanding Paper Award at the 2014 Annual Conference of the American Association for Artificial Intelligence (AAAI), and the Edward K. Rice Outstanding Graduate Student.