$\chi$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
| Autor: | Poonia, Harsh, Willig, Moritz, Yu, Zhongjie, Zečević, Matej, Kersting, Kristian, Dhami, Devendra Singh |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ($\chi$SPN) that is capable of estimating interventional distributions in presence of random variables drawn from mixed distributions. $\chi$SPN uses characteristic functions in the leaves of an interventional SPN (iSPN) thereby providing a unified view for discrete and continuous random variables through the Fourier-Stieltjes transform of the probability measures. A neural network is used to estimate the parameters of the learned iSPN using the intervened data. Our experiments on 3 synthetic heterogeneous datasets suggest that $\chi$SPN can effectively capture the interventional distributions for both discrete and continuous variables while being expressive and causally adequate. We also show that $\chi$SPN generalize to multiple interventions while being trained only on a single intervention data. Comment: 17 pages, 11 figures. Accepted as poster at UAI (Uncertainty in Artificial Intelligence) 2024 |
| Databáze: | arXiv |
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