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statspai.neural_causal

neural_causal

Neural Causal Inference Models.

Deep learning approaches to treatment effect estimation that leverage representation learning to handle high-dimensional confounders and complex outcome surfaces.

Models
  • TARNet : Treatment-Agnostic Representation Network (Shalit et al. 2017)
  • CFRNet : Counterfactual Regression with IPM regularisation (Shalit et al. 2017)
  • DragonNet : Targeted regularisation for causal estimation (Shi et al. 2019)
  • CEVAE : Causal effect variational autoencoder for latent confounding

All models require PyTorch: pip install statspai[neural] # or: pip install torch

References

Shalit, U., Johansson, F. D., & Sontag, D. (2017). Estimating individual treatment effect: generalization bounds and algorithms. Proceedings of the 34th International Conference on Machine Learning (ICML). [@shalit2017estimating]

Shi, C., Blei, D. M., & Veitch, V. (2019). Adapting neural networks for the estimation of treatment effects. Advances in Neural Information Processing Systems (NeurIPS), 32. [@shi2019adapting]