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Causal Inference Through the Structural Causal Marginal Problem
- Source :
- International Conference on Machine Learning (ICML 2022), 7793-7824
- Publication Year :
- 2022
-
Abstract
- We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: given a collection of marginal structural causal models (SCMs) over distinct but overlapping sets of variables, determine the set of joint SCMs that are counterfactually consistent with the marginal ones. We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs. Our work thus highlights a new mode of falsifiability through additional variables, in contrast to the statistical one via additional data.<br />Comment: 32 pages (9 pages main paper + bibliography and appendix), 6 figures
- Subjects :
- Computer Science - Artificial Intelligence
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Journal :
- International Conference on Machine Learning (ICML 2022), 7793-7824
- Publication Type :
- Report
- Accession number :
- edsarx.2202.01300
- Document Type :
- Working Paper