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Model-completion methods learn a full causal generative model consistent with observational data and a given causal graph, and answer interventional queries via probabilistic inference. We empirically compare two approaches presented recently. One approach learns the model using EM, named EM for Causal Inference (EM4CI). The other approach uses neural networks for completion, yielding two neural causal model approaches, MLE–NCM and GAN–NCM. We evaluate these methods on synthetic discrete benchmarks spanning multiple graph families and scales. Results show that EM4CI seems superior on large graphs in terms of accuracy, while NCM-based methods can be competitive on small models but incur substantially higher computational cost.
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This content will become publicly available on May 5, 2027
An Empirical Evaluation of Model Completion for Causal Inference
Model-completion methods learn a full causal generative model consistent with observational data and a given causal graph, and answer interventional queries via probabilistic inference. We empirically compare two approaches presented recently. One approach learns the model using EM, named EM for Causal Inference (EM4CI). The other approach uses neural networks for completion, yielding two neural causal model approaches, MLE–NCM and GAN–NCM. We evaluate these methods on synthetic discrete benchmarks spanning multiple graph families and scales. Results show that EM4CI seems superior on large graphs in terms of accuracy, while NCM-based methods can be competitive on small models but incur substantially higher computational cost.
more »
« less
- Award ID(s):
- 2321786
- PAR ID:
- 10691176
- Publisher / Repository:
- AISTAT
- Date Published:
- Format(s):
- Medium: X
- Location:
- Morocco, Tanjir
- Sponsoring Org:
- National Science Foundation
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